Doctoral thesis of Philosophy: Role of beliefs and past experience in forming resort accommodation purchase behaviour: a study of Australian tourists

ROLE OF BELIEFS AND PAST EXPERIENCE IN FORMING RESORT ACCOMMODATION PURCHASE BEHAVIOUR: A STUDY OF AUSTRALIAN TOURISTS

A thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy Mukesh Sharma M. Buss (Hospitality Management), B. Sc. (Biology), Diploma of Hotel Management, Catering and Nutrition, Diploma of Teaching (TAFE) School Of Management Business Portfolio RMIT University, December, 2008

Declaration _____________________________________________________________________

I hereby certify that the work is of the author alone except where due

acknowledgement has been made. The work has not been submitted previously, in

whole or in part, to qualify for any academic award. The content of the thesis is the

result of work which has been carried out since the official commencement date of the

approved research program.

Mukesh Sharma

Date: 19 November, 2008

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Dedication ___________________________________________________________________

In Gita, Krishna turns to Arjun in the midst of the epic battle and tells him –

‘Karmanyeva dikaraste maa phaleshu kadachana’ meaning “Do your duty without

expecting rewards”.

I would like to dedicate the thesis to the loving memory of my parents - (late) Mrs

Shanti Sharma, a wonderful mother, happy home-builder and the best cook in the

whole world and my father, (late) Mr Atulesh Chandra Sharma, M.Sc. (Mathematics),

Bachelor of Engineering (Civil), FIIE, who practiced and passed on Lord Krishna’s

teachings to his children.

Papa, you will always be an inspiration to me.

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Acknowledgements

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This thesis has been completed with the guidance, encouragement and cooperation of

many people. First, I would like to thank Dr Robert Inbakaran, my friend, philosopher

and guide. I will forever cherish your fervent energy, the quest for knowledge and the

wisdom you have passed on to me. I also wish to note my deep appreciation for my

second supervisor Dr Mervyn Jackson the kindest person one can befriend, who

always had time for me with encouragement and the one who has taught me all that I

know about data analysis. Two other people who were not associated with the project

but were always there with support were Dr Raju Mulaye and Dr Prem Chettri. I will

forever remember you both for the clarity of thought and incessant encouragement.

I have nothing but praises for the Research Development Unit at the university

especially Ms Prue Lamont. Thank you for your efficient support.

I would like to thank my family for their encouragement, support and understanding.

Rashmi, my dear wife and a true friend, you have been wonderful through my journey

of learning and self actualisation. Tarika, my daughter and my sanity, thank you for

your prudent suggestions. I can see a potential human rights lawyer in you. Ritwik,

my son and my strength, you have the prudent disposition of a potential constitutional

lawyer. Thank you for letting me use the computer in your room at unearthly hours. I

would also like to thank my mother-in-law, Mrs Krishna Arora, for her

encouragement, support and blessings.

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I would like to thank my colleagues at William Angliss Institute who were always

there to help me achieve my dream. They are Kerrie Obst, Kerry Howley, Penny

Irons, Wendy Knowles, Terrie Shaw, Joy Vandoske, Tom McGee and David Miller,

in no particular order.

Finally, I would like to extend my sincere thanks to my friend and colleague, Peter

Smith for being there to listen to my ideas, reading my manuscript and suggesting

changes.

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Abstract 1.1.0 Background and overview of the present study 1.2.0 Aim of the Research 9

1.3.0 Justification of the Study 9

1.4.0 Significance of the Study 10

1.5.0 Research Questions 11

1.6.0 Limitations of the Scope of the Study 12

1.7.0 Organisation of the Study 13

2.1.0 Defining Resorts in Australia 2.2.0 Tourist Market Segmentation 22

2.3.1 Search Behaviour

2.3.2 Motives

2.3.3 Emotions

2.3.4 Fear and Risk Taking in Tourism

2.3.5 Expectations

2.3.0 Focus of Study in Recent Tourist Segmentation Literature 29

2.4.0 Role of Beliefs in Buying Behaviour 35

2.5.0 Past Behaviour 42

3.1.0 Research Design and Method 48

3.2.0 Construct Reliability and Validity 48

3.3.0 Source of Data and Sampling Procedure 49

3.4.1 Sample Size for Factor and Cluster Analyses

3.4.2 Sample Size for Regression

3.4.0 Sample Size 52

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3.5.0 Questionnaire Construction Approach 56

3.6.0 Ethics and Approval 57

3.7.1 Plain Language Statement

3.7.2 Demographics

3.7.3 TPB

3.7.3.1 Behavioural Intention

3.7.3.2 Attitude toward the Behaviour

3.7.3.3 Behavioural Beliefs

3.7.3.4 Subjective Norm

3.7.3.5 Normative Beliefs

3.7.3.6 Perceived Behavioural Control

3.7.3.7 Control Beliefs

3.7.0 Questionnaire Design and Construct 60

3.8.0 Past Behaviour 66

3.9.0 Data Screening 66

3.10.0 Data Analysis 67

4.1.1 Gender

4.1.2 Age Group

4.1.3 Education Level

4.1.4 Family Status

4.1.5 Age of Youngest Child

4.1.6 Occupation Category

4.1.7 Australian Residency Status

4.1.8 Language Spoken at Home

4.1.9 Resort Visitation in the last Three Years

4.1.10 First Resort Experience (History)

4.1.0 Sample Profile 73

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4.1.11 Usual Length of Stay

4.2.0 Investigating the correlations between beliefs constructs 76

4.3.0 Rational for the variables 87

4.4.1 Cluster 1 (Active Conventionalists)

4.4.2 Cluster 2 (Young Conservatives)

4.4.3 Cluster 3 (Elite Regulars)

4.4.4 Cluster 4 (Veterans)

4.4.0 Clusters Description 89

4.5.1 Cluster 1

4.5.2 Cluster 2

4.5.3 Cluster 3

4.5.4 Cluster 4

4.5.0 Cluster Description on Reasons, Activities, Past Experience and TPB 93

5.1.0 Resort tourists segmentation 5.2.0 Beliefs and Resort Tourist Purchase Behaviour 103

5.3.0 Past Experience and Resort Tourist Purchase Behaviour 106

5.4.0 Implications 108

6.1.0 Limitations 116

6.2.0 Recommendations 120

References 123

151

Appendix A Request to participate in the study letter – resorts Appendix B Questionnaire 153

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Abstract ___________________________________________________________

Hospitality industry has a long history for providing accommodation along with

recreation facilities to its patrons. Resorts are a more recent phenomenon in offering

similar services. The similarity stops there as the people who use resorts have

different expectations and motives to be there. While hotels are mainly used by the

business people and are busier during the weekdays, resorts are generally used for

vacation and rest and are busy during holiday season. The difference in the clientele’s

motivations makes it difficult for the resort marketers to effectively position and

market the property to the right segment. There have been many studies done

primarily on hotel clients, while resorts have largely been neglected.

This study investigates the role of beliefs held by the clients and their previous

experience of resort usage, when they choose resort accommodation for their

vacation. The beliefs investigated in this study are based on the Theory of Planned

Behaviour (Ajzen, 1985, 1991) where he proposed that human behaviour is formed by

three beliefs- benefit, normative and control. The Theory of Planned Behaviour is a

well established theory and has been used extensively in medical, psychology and

marketing disciplines. This study is the first step in evaluating the level of

contribution beliefs make when Australian tourists decide on their resort

accommodation purchase. To achieve this aim the resort market was segmented and

then every segment was tested on the model developed for the study.

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In this study, 412 people responded by filling out the questionnaires that were put in

their rooms, by the participating resorts they were staying in. The study targeted all

states and Territories of Australia. Every possible precaution was taken to maintain

the anonymity of the respondents and the participating resorts to avoid compromising

their financial interests.

The study found four segments of resort tourists. They were named active

conventionalists, young conservatives, elite regulars and veterans. The role of beliefs

and past experience in purchase decision was found to be of varying degrees amongst

the segments. It was also found that benefit beliefs had the bigger role in resort

selection compared to normative beliefs. Control beliefs had the least role in the

formation of purchase behaviour. It was also found that while the Theory of Planned

Behaviour was incapable of predicting resort accommodation purchase behaviour on

its own, the addition of past behaviour to the mix increased the predictability

perceptibly.

The main limitation of the research was that the researcher and the respondents were

far removed from each other. It is recommended that in future studies; there must be a

provision for qualitative data to complement the quantitative approach. Besides this,

there are many more important recommendations made relating to design and

application of the questionnaire for future studies. The study also stresses that similar

studies should be conducted, preferably on longitudinal basis to confirm or reject the

findings of the present study.

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The present study contributed to the body of knowledge by providing a theoretical

framework and suggesting a resort purchase predictability model incorporating beliefs

and past experience of resort tourists. It also provided resort marketing planners with

practical recommendations and implications in terms of attracting the right clients to

their resorts as well as how to position their resorts for the intended market segment to

get the best returns on their investment in marketing.

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Chapter 1 Introduction

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Chapter Overview _____________________________________________________

This chapter lays down the foundation for the thesis. It discusses the

background of the study as there is a difference between a hotel and

a resort property. They both provide accommodation but the people

who go to these two have different motivations and expectations.

The resort operators are forever investigating how to market their

properties to get the best returns on their marketing expenditure.

The chapter also explains the aims and objects, the limitations and

the layout of the whole thesis.

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1.1.0 Background and overview of the present study

Tourist resort hotels have been considered as one of the vital, service orientated,

leisure tourism products in recent decades. In fact they have come to dominate the

accommodation segments in the tourism industry all around the world on the basis of

their special services and functions since the mid 1960s. While resorts have been in

existence dating back to medieval times, resort hotels have a very short history and

their proliferation as an indispensable part of the tourism industry has happened only

with the economic boom experienced in many western economies from the 1960s

(Papatheodoru, 2004). While tourist resorts are considered hotels as they offer similar

services to business hotels, they differ in service expectations (Mill, 2002) as hotels

are primarily used by the businessmen who expect the facilities to assist in their work

as seen in the existence of business centres in hotels and higher occupancy during the

week days compared to the weekends, where as the resort hotels focus mainly on rest

and recreation and they are frequented more during the typical holiday periods.

Therefore it is not surprising that most of the research has been focused on the hotels.

The main reason could be that they form a major part of the hospitality industry as a

sector besides they have been in existence for longer period of time as compared to

resort hotels.

Tourism is expected to generate $US 10 trillion of economic activity and 328 million

jobs worldwide by 2010. According to Tourism Forecasting Council (TFC) Australia

is likely to get 9.4 million international visitors in year 2010. Tourism accounts for $A

48.7 billion or 8.6% of Australia’s Gross Domestic Product (Tourism Victoria

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Strategic Plan, 2002-2006). Through the 1990s, the tourism industry has been

considered to be the largest and fastest growing industry in the world with all levels of

government (national, state, local) funding tourism boards to promote their locations.

Tourism researchers have primarily focused on travellers, their needs, behaviours and

their welfare (Krippendorf, 1987; Sharpley, 1994; Inbakaran, Jackson & Troung,

2004). It has also been stated that tourism is the largest peacetime movement of

people and that tourism has had an astonishing high annual growth rate to the year

2000 (Upchurch & Teviane, 2000). A sizeable number of tourists visit various

destination and non-destination resorts for their holiday needs while on their

international and domestic trips. In many ways tourist resorts of all types assist the

tourism destinations to attract the willing tourists with their novel packages and

enticing guest activity programming.

Many economies are dependent on the financial benefits that arise from the tourists in

the geographical area. To achieve a healthy flow of tourists through a community,

they continually seek ways to attract the right type of people to maximise the return

on their investment in the infrastructure. A better targeting of right segments result in

a better sales revenue. Therefore, it is paramount for resort marketing managers to

know who these people are and how do they get to the decision of buying resort

products and services. As a result, choice of vacation destination has become one of

the primary focuses of tourism management. The research focusing on tourists’

buying behaviour has discovered that individuals seek out the alternatives of the

destinations to choose from before deciding on the final one. It is accepted that people

first form a set of alternatives for tourism from their memory, then they evaluate each

alternative on a set criteria to reach a final decision. This choice set is referred to as

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the evoked set, while the whole bank of memory is referred to as the known set or the

awareness set (Crompton, 1992; Crompton and Ankomah, 1993; Um and Crompton,

1990, 1992 and Woodside and Lysonski, 1989). To a certain degree, beliefs held by

the buyers form the basis of their behaviour to select or reject a product or service.

Ajzen (1985, 1991) developed the theory of planned behaviour (TPB) to investigate

this phenomenon in the field of consumer behaviour and the psychology associated

with purchase. According to Ajzen (1991) human behaviour is guided by three kinds

of considerations; beliefs about the likely outcomes of the behaviour and evaluation of

these outcomes, beliefs about the normative expectations of others and motivations to

comply with these expectations and the beliefs about the presence of factors that may

facilitate or impede performance of the behaviour and the perceived power of these

factors. Furthermore, there have been suggestions that predictive power of the TPB

model can be enhanced by incorporating past experience in the paradigm (Abraham,

2001). Paradoxically some researchers have found TPB model to be too rigid to

explain the complex interplay of beliefs in destination selection with changes over

time and situations (Hudson, 1999; Litvin and MacLaurin, 2001).

There is very little academic research on tourist resorts and the reasons people choose

them. A very important development has taken place recently concerning tourist

research in Australia. For the first time, Inbakaran and Jackson (2005) have

segmented the domestic tourist market into four main groups. This research project

looks into the interrelationship of satisfaction, beliefs and perceptions that form the

buying behaviour of the segments proposed by Inbakaran and Jackson (2005) in resort

hotels.

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As mentioned before, the managers of the tourism and the researching academics

would like to know the motivating factors that propel a tourist to select a particular

destination. This dissertation aims to understand these factors better by applying the

theory of planned behaviour as proposed by Ajzen (1991). Specifically, this study will

investigate the interplay of the beliefs of the consumers and the past behaviour to

create a model that could be utilised by the tourism managers to place their resorts for

selection by the prospective buyers.

1.2.0 Aim of the Research

The main objective of the study is

• to segment the tourists using resorts in Australia

• to propose a conceptual model which describes the factors that affect the

travellers’ choice of resort hotels based on their beliefs along with the effect of

past behaviour and

• to test the model across the segments using multivariate data analysis

techniques.

1.3.0 Justification of the Study

Tourism generates a significant amount of revenue for many communities and

localities thus they continually keep searching for new ways of attracting potential

tourists as suppliers of such services are well aware of the competition they have

from other available; similar or dissimilar choices. The more they can tap into the

potential market, the better the returns are in terms of revenue. Therefore, it is

justifiably critical for the marketing managers of resort accommodation to know the

segment of the population who may be more attuned to and positively motivated to

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avail their type of resort accommodation and the attractions they offer. This

knowledge is crucial for their marketing efforts

Psychology based researchers on tourists’ destination choice behaviour in the past

have found that people usually consider different options before settling down for the

final destination choice. The investigation of the beliefs held by the prospective

consumers along with their past experience could be a very good predicator of their

behaviour.

1.4.0 Significance of the Study

This study captures the current research gaps and deficiencies in resort tourist

segmentations and the addition of psychographics to the published demographic

model. Its significance lies in both academic and practical aspects.

On the academic level, the outcome of the study may contribute to the current body of

knowledge in three areas,

• Firstly, as discovered and discussed in the literature review that research in

this area is very limited. This can be partially explained by the obvious lack of

suitable theoretical frameworks in the study area as this sector of tourism is

relatively new. Furthermore, this is the first study to employ theory of planned

behaviour in the context of a tourist’s buying behaviour for resort hotels. The

successful completion may enhance the understanding of resort hotels from

customer beliefs point of view and contribute a theoretical model suitable to

understand and predict the resort accommodation purchase behaviour in other

geographical settings.

• Secondly, the introduction of past experience will provide more

comprehensive understanding of purchase intention behaviour in resort hotels.

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• Thirdly, the model that evolves out of this study will be used for further

confirmation or replication in future studies.

• Finally, it will strengthen interest in the sector and provide a platform to

conduct further studies by other scholars.

In practical aspects the study will contribute in the following areas.

• Firstly, this study will help the operators of resort hotels to understand the

tourist segments for their specific factors that mould their purchase intention

behaviour.

• Secondly, the operators will be able to make required changes and position

their products to suit the needs of the specific segment they want to attract.

• Finally, they will be able to market their property to the right segment, based

on the existing attributes, thus saving on unnecessary developments /

modifications their marketing budget and getting the optimum returns on their

marketing expense.

1.5.0 Research Questions

• What is the composition of resort tourist segments on the basis of their

demographics, reasons for visiting a resort and the activities they indulge in?

• To what degree the normative beliefs held by of the resort vacationer

segments’ decision to purchase their leisure resort accommodation?

• How do the behavioural beliefs affect the accommodation buying intentions of

the resort tourists segments in Australia?

• Do the perceived behavioural control beliefs of the resort market segments, at

times, by-pass their behaviour intentions when making the purchases?

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• What is the effect of past purchases of resort hotel facilities on the future

purchase intentions of such facilities?

1.6.0 Limitations of the Scope of the Study

There are many limitations to the study. The primary one is that the industry itself is

not homogenous. The most difficult one to fathom is the effect of the ownership

structure and the cultural orientation of the owners. This study also does not

investigate the factor whether the resort hotel belongs to a chain or is a stand alone

property. This factor is important as they are usually operated with different

perspectives. The resort industry spans across the globe. The multinational resort

chains have a very broad customer base as they market their hotels to the customers in

various countries. Similarly, an Australia based resort chain may have a completely

different customer profile that uses its offerings in Australia, as compared to the one

that may patronise its resort located in Europe, Asia or Americas. This contributing

factor is beyond the scope of this study.

The study focuses only on the resorts in Australia. Australia being a large continent, it

will be imprudent to assume that what is being offered in the northern parts of the

country will be similar to what is being offered in the southern parts as well as they

operate in completely different climatic conditions. Besides this geographical

difference, the differences of the location of the resort in one area will differ

according to altitude, local tourist interests, purpose of visit and the season of their

utilisation. These factors individually or collectively alter the way a resort is

perceived by the consumers. This study has a very broad scope and it is not

considering the resorts according to their classification, geographical or based on a

star system to be applicable to each and every resort individually.

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The major limitation of the study is that its results are based on the questions

considered to be important by the researcher and does not have an option to interview

the respondents to elicit their specific views that are being left unasked. This

limitation is acknowledged and accepted due to the fact that such enquiry will require

massive amounts of man hour and budget, way beyond the researcher’s or the

university’s resources.

1.7.0 Organisation of the Study

The study is organised in the following sequence. In the first chapter, the definition of

resorts, aim, justification and significance of the study are discussed along with the

research questions and the major limitations of the effort.

In the second chapter, the existing literature on the segmentation of tourists, theory of

planned behaviour and past experience is discussed. The study is focused primarily on

Attitude (Benefit Belief)

.5 Intention Behaviour

Subjective Norms (Normative Belief)

Perceived Behaviour Control (Control Belief)

Figure 1: Theory of Planned Behaviour Model (Ajzen, 1985)

the buying behaviour of tourists in Australia who avail resorts accommodation. To

achieve the aim of the study, the literature review focuses on; tourist segmentation,

the available literature on beliefs as proposed by Ajzen (1985) and past behaviour.

The marketing literature is full of many attempts to study the buying behaviour using

different constructs. This project uses the established and studied Ajzen’s (1985)

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theory of planned behaviour as the basis to construct buyer beliefs which provides it a

definite aim while giving it the scope, depth and focus and maintains academic rigor.

Although researchers have found positive relationships between buying behaviour and

the stated constructs (beliefs and past experience) individually, this is the first time the

constructs have been used in this combination to investigate resort accommodation

purchase behaviour by Australian tourists. The literature review looks at the

segmentation of tourist market first and then investigates the available literature on

beliefs. Finally, it goes through the previous studies concerning past behaviour. These

variables come together in the proposed model in this chapter. In the same chapter,

the hypotheses are also developed based on the theory. They are as follows.

Hypothesis 1:

Tourists using resorts accommodation can be segmented in various groups. Hypothesis 2: Positive attitude toward resorts by the tourists is directly linked to their accommodation purchase behaviour intention.

Hypothesis 3:

Positive subjective norm toward resorts by the tourists is directly linked to their accommodation purchase behaviour intention. Hypothesis 4:

Positive perceived behavioural control by the tourists toward resorts is directly linked to their accommodation purchase behaviour intention. Hypothesis 5:

Past experience of resort accommodation purchase by the tourists positively affects their behaviour intention.

The third chapter explains the research methodology in detail. In total, 348 resorts of

various types were approached for their cooperation for the research. The request

letters were sent to their postal addresses and that was followed up with a phone call

to explain the nature and scope of the research to the managers / owners. Finally, 43

establishments agreed to participate. Each were sent 50 questionnaires with stamped

envelops. After a month, 257 filled out questionnaires were received. Another round

of telephone calls elicited further 178 responses. Out of the total 23 were found to be

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unusable. The chapter goes on to elaborate the rationale behind the questionnaire

construction such as demographics related questions in one section, TPB related

questions in the second section and past experience related questions in the third

section. SPSS package was used to analyse the data.

The fourth chapter describes the data analysis. A frequency analysis table is presented

to describe respondents’ demographic characteristics. The groups were formed from

the data using the cluster analysis (K-means). An analysis was conducted to

investigate the correlations amongst belief constructs in the TPB model. To model

was tested using ANOVA for regression analysis and new model was developed to

improve the predictability of resort accommodation purchase behaviour. It also shows

how different segments found in the study measure up on the model construct to

predict their purchase intention behaviour individually.

The fifth chapter discusses the four clusters that arise out of analysis. Their reasons

for going to a resort along with the activities they indulge in. The whole sample was

tested on the original model and the proposed model. An improvement in the

predictability of their purchase behaviour was noticed. Further, the clusters were

separated as individual files and the predictability of the proposed model to explain

the variance. The study comes up with interesting and distinguishable clusters which

is a good sign as these clusters can be individually approached for marketing

purposes. The study proposes the following model as improvement over the TPB

model.

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Past Experience

Attitude

Behaviour Subjective Norms

Intention

Perceived Behaviour Control

Figure 3: Resort Accommodation Purchase Behaviour Model

In chapter six, limitations of the research are discussed as well as further

recommendations are made. In short it maintains that the study is limited only to the

people who were at a resort at the time of data collection. It would have been better to

include the people who have experienced resort accommodation in the past to capture

the information based on their reflection. The study does not make any attempt to

include the cross cultural comparisons although such differences exist and they do

affect range of beliefs. The response rate was low. This could be attributed to the

length of the questionnaire as well as the geographical distance between the

respondents and the researcher. Further, it was dependent on the interest and degree of

support the managers / owners of resorts had for the study as they are generally very

parochial about their clients. The study considers two separate measurements of

behaviour ‘intention’ and ‘attitude’ which potentially increases statistical error.

Finally, as the study investigates the behaviour only at the time of data collection, and

not over an extended period of time to study variances that may creep in, the results of

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this study should be treated with caution till such time they are replicated in other

studies.

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Chapter 2 REVIEW OF LITERATURE

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Chapter Overview _____________________________________________________

This literature review prepares the basis for hypotheses

development. In this chapter, the definition of resort and its meaning

are debated to make the reader understand the scope of study. The

chapter then discusses the various attempts made in the field of

tourist segmentation. Next, it talks about the available literature on

beliefs as proposed by Ajzen (1991) and finally studies investigating

past behaviour are examined. This chapter establishes that although

academics have found positive relationships between buying

behaviour and the stated constructs individually, this is the first time

these have been used in this combination to investigate resort

accommodation purchase behaviour by Australian tourists. It

proposes five hypotheses; one on segmentation of Australian resort

tourists, three on the theory of planned behaviour and its

relationship with the resort tourists’ accommodation purchase

behaviour and intention and one on past behaviour and its impact on

the improvement on purchase behaviour predictability.

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2.1.0 Defining Resorts in Australia: The resorts are defined by the Australian Bureau of Statistics (ABS) as:

‘Establishments which are integrated complexes containing

accommodation and a variety of eating and drinking places. These

establishments provide facilities / services additional to those commonly

provided by hotels or motels. They may encompass some natural physical

amenities, a special location, attraction or activity. They provide

accommodation on a room / suite / cabin / unit basis. These

establishments provide sufficient night life and day time activities to

encourage an extended, self-contained, on –site holiday’ ABS (1989).

However, resorts are defined and identified differently by the industry. Weigh and

Gibbings (1991) while reviewing the performance of the accommodation sector

focused on hotels, motels and caravan parks yet alluded to the future role of resorts

identified as destinations and genuine contenders for product development. In general

the academic definition of resorts has tended to be pragmatic. Gunn (1988:108)

defines resorts as ‘complexes providing a variety of recreations and social settings at

one location’. This does not refer to tourism or tourists specifically. Another

definition by Burkart and Medlik (1985:14) is still very general but does refer to

tourism when they say that ‘gradually the term resort has come to acquire its literal

meaning to denote any visitor centre to which people resort in large numbers’. The

authors then proceed to include capital cities within the definition on account of their

role of centres of commerce. Leiper (2004:7) refers to resorts as ‘a destination,

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normally not far away, for recreational purposes – for rest, relaxation and / or

entertainment’ which people visit temporarily.

Although governments have tried to define resorts such as in case of Queensland’s

Sanctuary Cove Resort Act 1985 and its subsequent amendment in 1987 which was

later elaborated into the state-wide Integrated Resort Development Act 1987, the

primary concern and aim of such effort has been the investors only. The responsibility

of defining and classifying resorts has fallen on the private sector. The major

contributors are the motoring organisations, tour operators and the resorts themselves.

This situation has given rise to the application of different standards to classify an

accommodation provider as a resort. ‘As there are no legal or regulatory restrictions

on what a property may call itself, or what a tour operator can describe a property

as. Even a caravan site can describe itself as a resort and in fact, many do’ (King and

Whitelaw, 2003: 60).

White (1985) acknowledges that motels can be resorts as well. His definition of the

resort motel depends on what facilities are available around it rather than the facility

being a part of an integrated complex. He defines resort motels as follows

‘A resort motel caters for travellers wishing to stay on extended time in

one locality in order to take full advantage of the local attractions …

Ideally a resort motel should be located near large, open, public spaces

such as parks, golf courses, lakes, rivers, beaches or man made

attractions.’ (pp. 108-9)

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A stricter definition is provided by the Royal Automobile Club of Victoria which

specifies what must be included ‘on the premises’ to merit resort categorisation.

Resorts are described as those establishments as ‘A property in spacious grounds …

providing meals and a wide range of recreational activities. It should have full time

activities staff / guides, a tour activities desk and a variety of eating outlets’ (Source:

RACV Accommodation Guide 06/07:25). It includes hotels, motels and caravan parks

under this classification. The guide gives some pointers as to what could be classified

as a resort, however, this does not preclude a property from calling itself a resort or

advertising itself as a resort in the guide to take advantage of consumer perceptions

for marketing purposes.

Facing such a dilemma, the study focuses on the star rating sources and picks various

properties from all states and territories of Australia. This approach captures both self-

drive tourists and the ones who are transported to the accommodation by airline and

coach companies. Although, due care has been taken and extensive study of the resort

properties has been done, it is still possible that a hotel or a motel may be referred to

as a ‘resort’ by the respondents. However, the perception should not have a major

effect on the final data as the respondents were approached at an establishment

identified at a resort by the researcher.

2.2.0 Tourist Market Segmentation:

The concept of market segmentation to the field of marketing was introduced by

Smith (1956:6). He provides the definition of market segmentation as ‘market

segmentation … consists of viewing a heterogenous market (one characterized by

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divergent demand) as a number of smaller homogenous markets’. The aim of

segmenting a market is to identify or put groups of people in clusters on predefined

parameters. This helps in studying them in depth as a single entity. In case the group

or segment is of considerable size then the marketing mix of product or service

offered could be modified to cater for the needs of the said market. Thus the biggest

benefit of market segmentation is to acquire a competitive advantage in the market

place as the product / service is positioned knowing reasonably well in advance that it

will be bought by the identified group while maintaining advertising efficiency.

Kotler et al.(2004:345) maintain that ‘markets consist of buyers, and buyers differ in

one or more ways’. As the buyers differ in their requirements, disposable income,

area, attitudes to a product and the way they buy them, each segment has unique

needs and wants, thus it is important to research them and package the products and

services tailored especially for them.

There have been over 50 attempts to segment the tourism market in general but there

has been minimal published research on resort visitors (Jackson et al, 2003). The fact

segmentation assists in marketing to the needs of a particular group (Dodd and

Bigotte, 1997, Snepenger, 1987), lowers the costs and increases effective penetration

of appropriate promotional material (Dodd and Bigotte, 1997). Tourist segmentation,

in the general sense, has been done on the basis of demographics (Dodd and Bigotte,

1997, Field, 1999, Jeffrey and Xie, 1995 and Kim et al, 2003), geographic (Formica

and Uysal, 1998, Frew and Shaw, 1999, Moscardo et al, 2000, Spotts and Mahoney

1993), psychographic (Coupal et al, 2001, Galloway, 2002) and behaviour (Bonn et

al, 1999, Cordoso et al, 1999 and Park et al, 2002).

________________________________________________________________ 23

Australia has a holiday culture but there has been very limited research done in this

country. In reviewing the available literature of the last ten years it was found that

very limited study has been done on Australian tourists. Of the 50 articles considered,

only six were based in Australia (Charters and Ali-Knight, 2002; Dolnicar, 2005;

Inbakaran and Jackson, 2005; Inbakaran, Jackson and Chettri, 2004; Kim et al., 2003

and Mohsin, 2005).

The following table depicts the recent segmentation studies in different countries.

Researchers

Countries Australia Austria Barbados Canada Canary Islands Number of Studies 6 2 1 1 1

Czech Republic France 1 2

Germany Guam Hong Kong Italy Kenya Norway Philippines Scotland Spain 1 1 1 1 1 1 1 1 5

South Africa South Korea Taiwan Thailand 1 1 1 1

Turkey UK USA 1 2 13

Inbakaran et al (2005) Dolnicar, 2002; Dolnicar and Leisch, 2004 Ibrahim and Gill, 2005 Carmichael and Smith, 2004 Diaz-Perez, Bethencourt-Cejas and Alvarez-Gonzalez, 2005 Orth and Tureckova, 2002 Jung, 2004; Lee, Morrison and O’leary, 2006 Baloglu and Uysal, 1996 Mok and Iverson, 1999 McKercher and du Cros, 2003 Trunfio et al, 2006 Beh and Bruyere, 2007 Mehmetlogue, 2007 Liu, 1999 Frochot, 2005 Bigne and Andreu, 2004; Diaz-Martin, Iglesias, Vazques and Ruiz, 2000; Gonzalez and Bello, 2002; Juaneda and Sastre, 1999; Molera and Albaladeja, 2006 Bloom, 2005 Hong, Kim and Kim, 2003 Chang, 2006 Phetvaroon, 2006, Hoontrakul and Sahadev, 2008 Alvarez and Asugman, 2006 Goulding, 1999; Prentice et al, 1998 Baloglu, Weaver and McCleary, 1998; Brown, 2003; Hu and Yu, 2007; Hudson and Ritchie, 2002; Leisen, 2001; Littrell

________________________________________________________________ 24

and Song, 2004, Kim and Jogaratnam, 2003; Mathur, Sherman and Schifferman, 1998; Moskowitz and Krieger, 2003; Mykletun, Crotts and Mykletun, 2001; Park et al, 2002; Spence and Holecek, 2007 and Zafar, 1996), Kenya (Beh and Bruyere, 2007

The present study investigates resort tourists that are of Australian origin and thus fill

the gap in the body of knowledge.

Scholars have considered various attributes to segment the tourist market such as

online / offline tourism information search behaviour (Alvarez and Asugman, 2006),

push and pull (Baloglu and Uysal, 1996), senior residents (Baloglu et al, 1998,

Goulding, 1999, Litterll et al., 2004, Kim et al, 2003 and Mathur et al, 1998), wildlife

and nature (Beh and Bruyere, 2007, Mehmetoglu, 2007, Prentice et al, 1998 and Hong

et al, 2003), emotions (Bigne and Andreu, 2004, Gonzalez and Bello, 2002),

Aboriginal culture and culture (Chang, 2006, Kim and Jogarathnam, 2003,

McKercher and du Cros, 2003 and Dolnicar, 2002), wine tourism (Charters and Ali-

Knight, 2002), quality expectations (Diaz-Martin et al, 2000), expenditure (Diaz-

Perez et al 2005, Lee et al, 2006), fear (Dolnicar, 2005), rural life (Frochot, 2005,

Molera and Albaladeja, 2006), shopping (Hu and Yu, 2007, Lee et al, 2006, Littrell et

al., 2004), perceptions (Ibrahim and Gill, 2005) lifestyle (Inbakaran et al, 2004) image

(Leisen, 2001 and Zafar, 1996) benefits and value for money (Mykletun et al, 2001,

Moskowitz and Krieger, 2003) and vacation style typology (Orth and Tureckova,

2002) to name the major ones. None have made a concerted effort to explore

segmentation on the basis of beliefs and only one study has been undertaken on past

behaviour (Phetvaroon, 2006) to classify the tourist market. This study has been

designed to fill the gap left by scholars in the field of tourism.

________________________________________________________________ 25

Studies conducted in the field of tourism are as diverse as the topics discussed above.

The authors have considered a variety of sample sizes from as small as 146 (Littrell et

al., 2004) to as large as 333, 428 (Carmichael and Smith, 2004) or in the 5,000’s

(Dolnicar and Leisch, 2004, Spencer and Holecek, 2007). Most of the researchers

have used questionnaires while some have used structured interviews (Baloglu and

Uysal, 1996, Charters and Ali-Knight, 2002, Dolnicar, 2002, Hudson and Ritchie,

2002, Juaneda and Sastre, 1999, Lee et al, 2006, Prentice et al, 1998, Spencer and

Holecek, 2007), on-site observation and focus group interviews (Goulding, 1999) and

secondary or historical data (Carmichael and Smith, 2004, Mykletun et al, 2001 and

Peterson and Malhotra, 2000). The present study used a questionnaire and is based on

responses from 412 individuals.

The segmentation of tourists in the present environment is unique, more complex and

multi-dimensional than in the past. Demographics based on sex, age and income were

the primary tools of the researchers to classify the market segments. Liu, 1999:5,

rightly says that these days, one has to be much more sophisticated when segmenting

markets. ‘Specifically, cluster segments of the vacation market must be catered for

based on arrays of multi-opinioned needs and consumer characteristics’. This view is

transformed in the approach where the choice is not between ‘sun or fun holidays;

young or old; male or female; but rather, creative holiday combinations which

incorporate a cluster of market requirements’. The individual’s behavioural beliefs

are a strong motivating factor as found by many scholars (Alvarez and Asugman,

2006; Baloglu et al, 1996; Beh and Bruyere, 2007; Bigne and Andreu, 2004;

________________________________________________________________ 26

Dolnicar, 2005; Gonzalez and Bello, 2002; Ibrahim and Gill, 2005; Mohsin, 2005 and

Peterson and Malhotra, 2000). The resort industry faces the challenge of

understanding the components and composition of the segments to decide which

cluster to approach to gain a competitive advantage over other suppliers of similar

products and services.

Personal characteristics are widely used as predefined segmentation criteria for

market segmentation. These could include socio-demographics such as students

verses retired people, behavioural variables such as repeat visitors verses first time

visitors and psychographic variables such as tourists interested in the local population

verses tourists attending a major sporting event (Dolnicar, 2008).

Market segments can be created in different ways. There are two popular and

established approaches. The first is a priori or commonsense segmentation (Alvarez

and Asugman, 2006; Dolnicar, 2004 and Inbakaran et al 2004). In this approach the

personal characteristics used to segment buyers are decided in advance In other words

the groups are formed according to a criterion that is expected to cause heterogeneity

of responses among the customers. A typical example of this is the tourists’ country

of origin. On the other hand a ‘response based’ approach forms groups by identifying

patterns of responses given by customers (Dolnicar, 2004).

The terms used to describe response based approaches can be post priori (post hoc) or

data-driven segmentation (Carmichael and Smith, 2004; Dolnicar, 2004; Hoontrakul

and Sahadev 2008; Mykletun et al 2001 and Spencer and Holecek, 2007). Numerous

publications list and evaluate these approaches in a comprehensive manner (Arabie et

________________________________________________________________ 27

al., 1996; Baumann, 2000; Dickinson, 1990 and Punj and Stewart, 1983). In the

response based approach multiple variables are used to form market segments. These

could be a set of ten tourism motives or a set of six behaviours related to tourism.

These variables provide a base for the segmentation and are used to create groups of

similar respondents. The resulting segments have to be interpreted and understood

well before they are named. This approach is fundamentally an exploratory data-

driven one. In tourism, cluster analysis is usually employed to identify or construct

segments (Alvarez and Asugman, 2006; Beh and Bruyere, 2007; Bigne and Andreu,

2004; Brown, 2003; Carmichael and Smith, 2004; Chang, 2006). A typical example of

this approach is benefit segmentation.

The use of the a priori approach is favoured to split customers into homogenous sub-

groups in terms of customer response if it is known from prior research or experience

as to what variables to use as it is simple to use and provides answers for the problem

at hand. If that is not the case then tourism managers need to explore the ways in

which homogenous response sub groups can best be constructed to form the data at

hand. According to Dolnicar (2004) the grouping techniques are dependent on many

considerations. The selection of variables those are included in the researching

procedure. The grouping technique is to be employed. The similarity of measures

those are appropriate and uniform. The number of groups or clusters to be achieve in

the final solution. This could lead to either too few segments, that would be difficult

to reach once they are identified or too many segments that lose their individual

identity and are thus impossible to tap in the marketing effort. Finally, the researcher

should be confident that the grouping chosen is not purely a random solution.

________________________________________________________________ 28

While as a researcher the above points are pertinent, as a marketer there are a few

more considerations to be sought. Kotler (1991) states that the segments must be

mutually exclusive meaning that they must have recognisable attributes that set them

apart. The segments must be exhaustive so that they justify the marketing budget. The

segments also should be measurable. These are always considered while segmentation

of a population is undertaken. Additionally, they must be accessible and at the same

time substantial, so that returns on marketing investment are positive. Most

importantly, the segments should respond in a different manner to a marketing

strategy, that is, to marketing mix variables controlled by the marketer.

2.3.0 Focus of Study in Recent Tourist Segmentation Literature:

2.3.1 Search Behaviour:

An individual’s environment exerts strong influence on the way one searches for

information in the decision making process (Smith, 1994). Many researchers have

suggested that an individual’s physical, cultural and social environment along with his

/ her direct or indirect relations to this environment has a strong influence on the

decision making process (Mitchie, 1986). Goodall (1991) says the decision making

process is very much dependent on context. The desire to search for tourism

information for travel purposes can be brought about by activities such as viewing

advertisements, watching a movie containing holiday information, recommendation of

friends or a sudden inheritance (Nichols and Snepenger, 1988). The consumer

behaviour literature has made a distinction between internal, such as memory search

and information retrieval, based on prior consumption or information experience

________________________________________________________________ 29

(Engel, et al 1993) and external search processes that provide extra information

needed to make the decision. The cost one is ready to pay for such information

depends on the cost of information uncertainty (the possible consequences of not

knowing) and importance of the decision (Jenkin, 1978).

The composition of travellers in terms of individual verses family or other social

grouping with the type of decision and information search have been studied by

Gitelson and Crompton (1983) and Nichols and Snepenger (1988). The destination

choice, including information search behaviour while with family or friends at the

vacation destination, was investigated along with the influence of friends and relatives

on information search behaviour, was investigated by Gitelson and Crompton (1979).

Snepenger et al (1990) studied the information search strategies of ‘destination

naives’. The strong association between previous destination experience and its

impact on information search intensity was observed by Woodside and Ronkainen,

(1986). The Internet is more recent phenomenon and it is being used by marketers in

increasing numbers. The uptake of on-line information by the consumer has been

strong probably because there is negligible cost associated with it (Moe and Fader,

2004). With this increase the researchers have also started segmenting the market

based on on-line purchase behaviour (Jainszeewski, 1998; Chen and Cooper, 2001;

Moe and Fader, 2004). In 2006, Alvarez and Asugman segmented the tourism market

in Turkey on the basis of their search behaviour. They formed two specific segments

risk-takers and risk-averse. Risk-takers were found to use less information sources to

plan their holidays and value the experimental aspects of the holiday and are more

likely to return to previously visited destinations. Another interesting study of on-line

search behaviour focusing on Thailand was conducted by Hoontrakul and Sahadev

________________________________________________________________ 30

(2008). They profiled the customers and provided some suggestions to convert the

inquiry into purchase by altering the supply side variables such as providing more

attractive room rates, enrolling more hotels etc.

2.3.2 Motives:

Many scholars have studied motives as a basis of segmentation. Most of them have

been around the concept of ‘push’ and ‘pull’ factors. This concept was explained by

Uysal and Hagan (1993) as a force that pushes or pulls people to take travel decisions.

The concept of push has traditionally been explained as the desire for travel while pull

has been the destination choice (Crompton, 1979; Christensen, 1983). They are

further explained by Uysal and Hagan (1993). The ‘push’ factors are intangible or

intrinsic desires of the traveller such as the desire to escape, rest and relaxation, health

and fitness, adventure, prestige and social interaction. On the other hand, ‘pull’ are

tangible factors such as beaches, recreational facilities and historic resources as well

as travellers’ expectations and perceptions. While Crompton (1979) identified nine

motivations to travel, Yuan and McDonald (1990) studied twelve factors, Pyo et al

(1989) investigated four dimensions, and Oh et al (1995) studied five factors. Some

scholars have looked at motives as ‘benefit seeking’ (Driver et al, 1987, 1991;

Manning 1986; Haggard and Williams, 1991), some have gone in search of legacy

and culture (Ivnko, 1996; Silverberg, 1995; McCain and Ray, 2003; Chang, 2006),

while others have studied motives such as rural shopping (Oppermann, 1995;

Kastenholz et al., 1999; Murdoch et al, 2003; Carmichael and Smith, 2004), green,

rural and cultural experience tourism (Richards, 1996; Blackwell, 1997; Miller, 1997;

Prentice et al. 1998; Kemmerling Clack, 1999; Frochot and Morrison, 2000; Ryan et

________________________________________________________________ 31

al. 2000; McKercher and du Cros, 2003, Hong et al, 2003 Frochot, 2005;

Mehmetoglu, 2007) and recreation (Rothschild, 1984; Selin and Howard, 1988;

McIntyre, 1989; Beckman and Crompton, 1989; Robinson, 1992; Park et al, 2002).

In a recent study, Beh and Bruyere (2007), investigated eight visitor motivations;

general viewing, nature, culture, adventure, mega-fauna, escape, learn and personal

growth to segment the market in three wild-life reserves of Kenya. They analysed 465

questionnaires and found three segments who they labelled escapists, learners and

spiritualists.

2.3.3 Emotions:

Tourism as a leisure activity is based on emotions. Studies have shown that

interaction with service providers increases the satisfaction in the buyers (Mittal and

Baker, 1998; Winsted, 2000). Over two decades ago, consumer behaviour researchers

started addressing the role of emotions in consumption (Peterson et al, 1986). Many

academics have studied emotions within the marketing discipline (Bagozzi, 1997;

Dube and Menon, 2000; Smith and Bolton, 2002) with its theoretical and practical

implications. The use of emotions as a segmentation base is still a relatively new

approach (Hirschman and Stern, 1999, Liljander and Strandvic, 1997; Westbrook and

Oliver, 1991). Bigne and Andreu, 2004 segmented the tourists on the basis of

emotions. They collected 400 questionnaires from tourists in Spain and named their

segments, angry-satisfied; unhappy-happy; dissatisfied-very pleased; sad-joyful;

disappointed-delighted; bored-entertained; depressed-cheerful; calm-enthusiastic;

passive-active and indifferent-surprised. This was a very good example of

________________________________________________________________ 32

segmentation based on consumer emotions evoked by the enjoyment of leisure and

tourism services. Their findings were that when people experience greater pleasure

and arousal emotions transformed the experience into a greater level of satisfaction,

thus increase loyalty and the willingness to pay more.

2.3.4 Fear and Risk Taking in Tourism:

Fear as an element of perceived risk was first studied in a consumer behaviour context

by Bauer (1960) Zuckerman (1964) studied the opposite of fear in sensation seeking

in a study which is still being used in the adventure tourism area. The idea is based on

the premise that the perception of risk is related to tourists’ competence or

investigated from a thrill – seeking perspective or the level of risk / thrill sought by an

individual. The concept of intrinsic risk was proposed by Bettman (1972) which can

not be managed by information search and risk reduction techniques when a consumer

makes a decision. Bettman’s (1972) typology makes the basis of studies in fear and

consumer behaviour. Developing on the previous work Bouter et al (1988)

investigated the downhill skiers. It was thought that people who seek thrill will be

more prone to injury but their findings were found to be the contrary. The reason for

such finding was thought to be based on the assumption that thrill seekers were more

experienced skiers. Adventure tourists in search of sensation were investigated by

Cronin (1991). It was found that the mountain climbers score high on the scale. Roehl

and Fesemnaier (1992) studied risks and found seven categories; equipment risk,

financial risk, physical risk, psychological risk, satisfaction risk, social risk and time

risk. Cossens and Gin (1994) found a direct link between fear of HIV infection and

destination choice. An association amongst past behaviour, and perceived risks was

________________________________________________________________ 33

seen by Sommez and Graefe (1998) while studying the intention to travel to certain

destination. Pizam et al (2002) found strong empirical evidence for the association of

risk and sensation seeking with travel behaviour and the choice of leisure activities.

In an interesting study, Dolnicar, 2005 used fear, a human emotion, as a segmentation

basis. She studied 373 Australian tourists and came up with four segments namely;

high-fear, thrill-seekers, overseas sceptics and low-fear. In the present state of fear

where the tourists are constantly made aware of the possibilities of both physical

harm, in the shape of a terrorist attack, or equipment risk, where the tourists could be

harmed by infections such as bird flu, AIDS or Hepatitis C. The marketing approach

based on fear is a real tool that can be used for marketing opportunities for the

domestic Australian tourists.

2.3.5 Expectations:

In a recent study Inbakaran and Jackson (2005) made an exhaustive study on resort

patrons within Australia. The holidaying resort guests were interviewed in various

tourist resorts. A total of 1,100 resort tourists were interviewed and 774 were

considered. The focus of the study was to segment the domestic resort tourists’ in

distinguishing groups on the basis of their demographics and reasons for choosing the

particular resort. The resultant resort tourist segments were compared on reasons for

resort selection in general, resort preferences, resort vacation opinions, resort service

expectation and overall satisfaction. The author generated four distinct cluster groups

of domestic resort tourists and named them as romantics, immersers, tasters and

________________________________________________________________ 34

veterans. The clusters differed on the basis of age, education, lifecycle and duration of

patronage.

The subjective interpretation of resort tourist segments and labelling by Inbakaran and

Jackson (2005) needs to be investigated as the labels have been their own assumptions

and it is expected the addition of psychographics to this clustering scheme will deliver

different segments as they have not objectively tested if these differences are indeed

present / occur in their clustering scheme. Although this was the first attempt at

segmenting the Australian market, the segmentation scheme was based on

demographics. The segments were labelled based on a speculation of the likely

lifestyles of the groups that were clustered on the basis of demographic variables. This

study will take this further by testing them against the proposed variables.

Hypothesis 1:

Tourists using resorts as a destination can be segmented into various

groups.

2.4.0 Role of Beliefs in Buying Behaviour:

Since the focus of this thesis is on consumer decision making, it will draw on the

theory of planned behaviour (TPB) (Ajzen, 1985, 1991) which has been a dominant

paradigm in the consumer behaviour and psychology areas. According to Ajzen

(1991), human behaviour is guided by three kinds of considerations: beliefs about the

likely outcomes of the behaviour and evaluations of these outcomes (behavioural

beliefs), beliefs about the normative expectations of others and motivation to comply

________________________________________________________________ 35

with these expectations (normative beliefs), and beliefs about the presence of factors

that may facilitate or impede performance of the behaviour and the perceived power

of these factors (control beliefs). In their respective aggregate, behavioural beliefs

produce a favourable or unfavourable attitude toward behaviour, normative beliefs

result in perceived social pressure or subjective norm and control beliefs give rise to

perceived behavioural control.

Attitude

.5 Intention Behaviour Subjective Norms

Perceived Behaviour Control

TPB is defined as a tendency to evaluate an object with some degree of favour or

disfavour (Eagly and Chaiken, 1993). Attitude held by people as a predicator of

human behaviour has been studied under this theory to a great extent. Researchers

have focused on the travellers’ attitude towards a destination and the relationship

between this attitude and their actual behaviour. It has been generally believed people,

who have a positive attitude, are more likely to engage in supporting or approaching

the target of such attitude.

The theory of reasoned action (TRA) has been used to express the relationship model

stream of attitude-behaviour (Ajzen, 1991; Ajzen and Driver, 1992). This theory

________________________________________________________________ 36

suggests that the intention is a precursor of behaviour; therefore it is the determinant

factor for behaviour. At the same time the same theory suggested that intention is

influenced by personal beliefs about the behaviour and by social influence. To put it

in other words, intention refers to a person’s willingness or motivation to carry out

behaviour, and this behavioural intention is determined by attitude and subjective

norms (Fishbein and Ajzen, 1975). Attitude is a cognitive evaluation of behaviour and

subjective norms as what an individual thinks and whether or not others in the social

or family circle condone such behaviour (Chiou, 2000). Taylor and Todd (1995)

believe that attitude and subjective norms are related to belief structures, and each

belief is combined with the importance of the desirability of outcomes from

performing the behaviour and the importance of following others’ opinions

concerning the behaviour.

The above theory is supported by empirical studies that showed a high correlation

between the intention to engage in chosen acts and the combination of attitude toward

behaviour and subjective norms (Albarracin et al. 2001; Conner and Armitage, 1998).

However, several researchers have found that there is a weak relationship between

intention and behaviour if there are some impediments to performance (Ajzen, 1991).

When these conditions prevail, the theory of reasoned action becomes impractical in

predicting the relationship between intention and behaviour because the theory

assumes that there should be no obstacles in the way (Eagly and Chaiken, 1993).

The theory of reasoned action was extended to become a theory of planned behaviour

just to counter such problems raised by the presence of the obstacles. The perceived

behaviour control was added by Ajzen (1991) as the third component. The perceived

________________________________________________________________ 37

behavioural control (PBC) refers to the extent to which a person believes that he or

she can perform the behaviour or the perceived amount of resources and control

which he or she has over the behaviour. Thus Ajzen (1991) proposed that intention

can be predicted not only from attitudes and subjective norms but also from perceived

behavioural control which directly influences the action. The addition of this third

element provided some excellent results in predicting human endeavours: pollution

reduction behaviours of managers (Cordano and Frieze, 2000), ethical behaviours of

sales persons (Kurland, 1996), physical activity participation by elementary school

children (Mummery et al. 2000), smoking, drinking and drug use by adolescents

(Grube and Morgan, 1990), purchase behaviour by students (Chiou, 2000), drinking

behaviours of university students (Wall et al. 1996), recreation behaviour of students

(Ajzen and Driver, 1992) and behaviours of hunters (Hrubes et al. 2001).

When Ajzen (2001) proposed that attitude is based on expectancy – value model, he

acknowledged that overall attitude can be automatically accessed without cognitive

efforts when a target is related to a previously stored object. In addition he agreed that

while attitude is not stable, contexts or current-activated goals can vary the

accessibility of beliefs, saying that ‘various contextual factors can temporarily make

certain beliefs more readily accessible (p. 32)’ and ‘people evaluate objects in relation

to currently active goals (p. 35).’ Similarly, Duran and Trafimow (2000) also

suggested that people may have an entire set of ‘for’ and ‘against’ criteria to evaluate

the entire concept. These may access the underlying attitude first before contacting

self-beliefs.

________________________________________________________________ 38

Although one advantage of the TPB is its economical approach to purposive

behaviour, its sufficiency has been questioned. For example, researchers proposed

additional components to address self identity processes (Sparks and Shephard, 1992),

moral norms (Parker et al, 1995), distinction between perceptions of control and

perception of self-efficacy (Armitage and Conner, 1999) and anticipated emotions

(Parker et al., 1995). Having said that, the TPB has been widely used with satisfactory

sufficiency in social psychology, and the model has been supported by many studies.

In the context of hospitality and tourism, the TPB has been employed to predict

individual’s behaviours to hotel marketing and social psychology studies (Ajzen and

Driver, 1992; Oh and Hsu, 2001; Lam and Hsu, 2005), but seldom used in social

science research related to domestic tourists’ attitudes and behavioural intentions,

particularly in an Australian setting. Hence TPB in the form of beliefs has been

proposed to be a part of the present study.

Table 2.1.0 Summary of TPB Application in Hospitality and Tourism Research

Intention

Author

Relationship

Year 1992 Ajzen and Driver Leisure choice

2001 Oh and Hsu Gambling

2005 Lam and Hsu Travel destination choice (BI+PBC)-B BI-B PBC-B (AT+SN+PBC)-BI AT-BI SN-BI PBC-BI BI-B AT-BI SN-BI PBC-BI PB-BI PB-BI AT-BI SN-BI PB-BI

Correlation .78 .75 .73 .86 .54 .70 .80 .42* .10* .09* -.39~.40* .43* 1.20* .36 .28 .32

________________________________________________________________ 39

* Path coefficient (p<.05); Multiple correlations; AT= Attitude toward behaviour, SN= Subjective norm, PBC= Perceived behaviour control, BI= Behaviour intention, B= Behaviour, PB= Past behaviour, BB= Behavioural belief, NB= Normative belief, CB= Control belief Attitude refers to the degree to which a person has a favourable or unfavourable

approach to the behaviour intention in question. According to Ajzen, 2001, it is

captured in attributes such as good-bad, harmful-beneficial, pleasant-unpleasant and

likeable-dislikeable. Rosenberg and Hovland, 1996:47, describe it as ‘all responses to

a stimulus object are mediated by the person’s attitude toward the object’. In

combination, attitude toward the behaviour, subjective norm and perception of

behavioural control lead to the formation of a behavioural intention. Behavioural

intention is defined as ‘the degree to which a person has formulated a conscious plan

to perform or not to perform specified future behaviour’ Warshaw and Davis,

1985:237. Warrington and Shim (2000) showed that behavioural intentions are a

precursor to the actual behaviour. Prislin and Quelette (1996) found highly embedded

attitudes were more strongly related to behavioural intention than lowly embedded

attitudes. Ajzen and Driver, 1992 opined that leisure choice intentions can be

predicted with considerable accuracy from attitude toward behaviour. Indeed there is

much evidence that suggests attitudes predict behaviour toward the target objects

(Albarracin et al., 2001). Hence, if the previous studies have shown a positive

relationship between attitude and behavioural intention, the same should be true for

the tourists’ attitude toward resorts and their behaviour intentions to use such facility

for their vacations. Therefore it is hypothesized that tourists that have a good attitude

toward resorts are more likely to use resorts for their vacation destination.

Hypothesis 2:

Positive attitude toward resorts by the tourists is directly linked to

their accommodation purchase behaviour intention

________________________________________________________________ 40

Subjective norms are the perceived pressure applied by a person’s beliefs or his / her

social circle’s expectations. They are social in nature as discovered by Hee (2000).

They are used by individuals to measure their conformity to the reference group

(Moutinho, 1987).The perception of social pressure refers to how an individual

performs or does not perform a specific behaviour. The more an individual feels

others expect of him / her in relation to a specific behaviour, the more he / she will

intend to indulge in such behaviour intention. Many studies have supported the role of

subjective norm to predict behavioural intention. While exploring the intention to use

Internet use to plan meetings, Vanucci and Kerstetter (2001) found a significant

relationship between subjective norms and behavioural intentions. In another study,

Buttle and Bock (1996) found a similar relationship in the hotel choice process of

business travellers. As recently as in 2005, Lam and Hsu found a similar relationship

while studying destination choice behaviour. As a general rule, the more favourable

the attitude and subjective norm, the stronger should be the person’s intention to

perform the behaviour in question. On the basis of the previous studies, it is

hypothesized that the more positive the normative beliefs of a person, the higher is the

possibility of behaviour intention while purchasing resort accommodation for their

vacation.

Hypothesis 3:

Positive subjective norm toward resorts by the tourists is directly

linked to their accommodation purchase behaviour intention

________________________________________________________________ 41

Finally, given a sufficient degree of actual control over the behaviour, people are

expected to carry out their intentions when the opportunity arises. The degree of

perceived behavioural control refers to a person’s belief as to how easy or difficult a

specific behaviour is. This belief is created with past experience of similar or related

acts as well as perceived difficulties and obstacles in performing such behaviour

intention (Lam and Hsu, 2005). Chiou (1998) in his study found that the perceived

behaviour control reflects the beliefs a person has as to availability and access to

resources and opportunities for performance of a behaviour intention. An additional

increase of a clear 6% in prediction of behaviour intention by considering perceived

behavioural control was found by Armitage and Conner (1999). While studying

gambling behaviour, Oh and Hsu (2001) also found a strong correlation between

perceived behavioural control and behaviour intention. Similar deductions were made

by Ajzen and Driver (1992) while studying leisure choice behaviour. Many other

studies investigating human behaviour with TPB have found a positive and direct

association between perceived behavioural control and behaviour intentions.

Therefore, the following hypothesis is proposed.

Hypothesis 4:

Positive perceived behavioural control by the tourists toward resorts

is directly linked to their accommodation purchase behaviour

intention.

2.5.0 Past Behaviour:

________________________________________________________________ 42

It was suggested by Conner et al. (2001) that additional construct will enhance the

predictive power of TPB. Many studies have used additional constructs with

significant improvement in behaviour intention and actual behaviour prediction.

Including anticipated regret improved the prediction for playing the lottery and

precautionary sexual behaviour (Richard et al 1998; Sheeran and Orbell, 1999). The

addition of personal or normal norms also improved the prediction of environmentally

relevant behaviour (Harland et al 1999; Manstead, 2000). While the addition of

personality traits improved the predictive power for Courneya et al (1999), the

addition of demographic variables failed to improve predictive power for Alderighi

and Cento (2004). However Ajzen (2001) found the inclusion of additional constructs

to be of minor importance and their applicability in other domains is yet to be

established. While Sparks and Guthrie (1998) support the sufficiency of the original

constructs of TPB, Norman et al (1999) argue in support of additional constructs. As

early as in 1979, Bentler and Speckart strongly suggested that past behaviour can

impact on the formation of behaviour intentions. Revisiting this stand; Ajzen (1991)

also believed that past behaviour can be used to test the sufficiency of a model

because it provides a control for at least some of the omitted variables.

The results from these studies are supported by Ajzen (1991) who stresses that ‘the

theory of planned behaviour, in principle, opens to the inclusion of additional

predicators if it can be shown that they capture a significant proportion of the variance

in intention or behaviour after the theory’s current variables have been taken into

account.’ Likewise, Conner et al. (1998:629) indicated that ‘since this TPB model

only covers the basic account of behaviour,’ additional variables are encouraged if

these variables can show ‘the theoretical description of their roles’ referring to

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specification of ‘the process by which the new variable influences intentions and

behaviour, its relationship to existing components of the TPB, and the range of

conditions over which such a variable might be expected to have an impact.’

Previous tourism studies have included the extra variables in predicting purchase

behaviour such as agreement within a party (Um and Crompton, 1990, 1992), social

influence and travel party influence (Basala and Klenosky, 2001), choice context

(Lawson and Thyne, 2001), interpersonal constraints (Hawkins et al., 1999; Iwasaki,

2000), cost, accessibility, political stability and value (Russell et al. 1995; Somez and

Graefe, 1998; Um and Crompton, 1990, 1992), for elderly their income, health and

mobility (Zimmer et al. 1995) and children as a type of inhibitor against the

participation in an activity or choosing a destination (Woodside and Lysonski, 1989).

There have been discussions about what past behaviour might represent. Human

behaviour theorists believe that best predictor of the behavioural intention is the

frequency of past relevant behaviour (Somez and Graefe, 1998). A meta-analysis

examined 64 studies that found the frequency of past behaviour had an effect on both

intention and future behaviours (Quellette and Wood, 1998). When behaviour is well

practiced in a constant environment, the frequency of past behaviour reflects the habit

strength and therefore has a direct effect on future behaviour. However, when

behaviour is not well exercised or when it is carried out in an unstable context, the

frequency of past behaviour contributes indirectly through intentions because people

are likely to form favourable intentions about the acts they have frequently performed

in the past (Quellette and Wood, 1998). It has also been found to be a predictor

variable of behavioural intention and future behaviour (Aarts, 1998; Ajzen, 2002; Oh

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and Hsu, 2001; Somez and Graefe, 1998; Taylor and Todd, 1995 and Yoo, 2004).

Thus it is reasonable to assume that the frequency of past behaviour could guide

future behaviour. In this study, it is hypothesized that the frequency of past purchase

of resort hotel products and services can affect future resort accommodation buying

intention.

Hypothesis 5:

Past experience of resort accommodation purchase by the tourists

positively affects their behaviour intention.

TPB has been used in various contexts to study behaviour intentions. It has been used

for studying drinking alcohol (Morrison et al. 1996; Traffimow and Finlay, 1998),

engaging in physical activity (Courneya et al. 1999), smoking (Norman et al. 1999;

Morrison et al. 1996), safe-sex behaviour (Boldero et al 1999; de Vroome et al 2000),

choosing a career (Vincent et al 1998), receiving hormone replacement therapy

(Quine and Rubin, 1997), wearing a helmet (Quine et al 1998), choosing restaurants

(Simone et al 2004) and choosing a travel destination (Lam and Hsu, 2006). Time and

again literature has validated the predictive power of TPB therefore it is being used in

the present study to investigate the application of beliefs and past resort

accommodation behaviour in predicting the purchase behaviour intentions of resort

tourist segments.

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Chapter 3 Methodology

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Chapter Overview _____________________________________________________

The chapter begins with the explanation of construct reliability and

its validity. It then explains the source of data and the sampling

procedure. The sample size achieved for this study is explained and

justified on the basis of previous studies for both cluster and

regression analysis. The questionnaire was developed on the basis of

a previous study by Inbakaran and Jackson (2005) and Ajzen

(2001). The study required ethics clearance from the Human

Research Ethics Sub Committee of RMIT Business Portfolio. The

committee sent the application back to the researcher and asked to

furnish some more details. The chapter goes through the detailed

reasons that were supplied to get a clearance to proceed with data

collection. The chapter goes on to explain the plain language

statement that accompanied the questionnaire and the main

attributes of the questionnaire. The elements relating to the TPB and

past experience are explained and their internal reliability has been

discussed. Finally, it explains how the data analysis was undertaken.

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3.1.0 Research Design and Method

This study is designed to examine the beliefs that form behaviour intentions for

ultimate resort accommodation purchase behaviour in each segment of the population.

The units of analysis are the beliefs and the observed units are the resort vacationers.

During the pre-test, 20 subjects from the place of work were asked to rate the

appropriateness of the items in each scale, the format of the scale and the length of the

instrument. Some items and scales were changed and the language simplified to

remove ambiguity. The questionnaire was formatted in a way that fit it into three

pages to reduce respondent fatigue.

3.2.0 Construct Reliability and Validity:

It is of paramount importance for the research instrument to have a high level of

validity and reliability. As Newman (2003) states, “reliability and validity are central

issues in all measurement. Both concern how concrete measures are connected to

constructs”. He further says that, “reliability and validity, the salient feature constructs

in social theory are often ambiguous, diffuse and not directly observable”. Perfect

reliability and validity are virtually impossible to achieve. Rather, “they are ideals

researchers strive to achieve” Newman (2003). Reliability means that the process is

repeated or recurs under similar or identical situations and there are no erratic,

unstable or inconsistent results. The pilot testing showed a high level of internal

reliability with Chronbach’s a higher than 0.70 (Nunnally and Bernstein, 1994).

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Validity is the concept that means there is a match between the construct and how the

researcher has conceptualized the idea into a definition and measure. “It refers to how

well the idea about reality “fits” with actual reality”, according to Newman (2003).

The instrument has a high validity as the TPB was measured based on the sample

questionnaire suggested by Ajzen (2001) which has been used by other researchers.

Researchers can establish construct validity by correlating a measure of construct with

a number of other measures that should be associated with it, also known as

convergent validity (Nunnally and Berstein, 1994). The standardised loadings and the

squared multiple correlations for the measurement items and the constructs were

investigated as a confirmation convergent validity (Bollen, 1989). Large factor

loadings for a specified construct suggest evidence of convergent validity,

demonstrating that indicators for a given construct are strongly correlated among

themselves.

3.3.0 Source of Data and Sampling Procedure:

Australia is a sun filled continent with its population positively attuned to the pursuit

of leisure activities. With the result there is large swath of the population that has

already experienced time away from home on a holiday. The industry has reacted to

the demand by offering various forms of accommodations in resort format. There are

different types of accommodations available for the users from a humble cabin in a

caravan park to rooms in five star deluxe multi national hotels, all providing a wide

spectrum of activities, services and facilities to help the guest enjoy their resort

experience They may encompass some natural physical amenities such as pools,

tennis courts, walking tracks or forest, a special location such as the beach, snow, the

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tropics, alpine or arid zones, attractions such as hot sulphur springs or activities such

as water, nature or snow based leisure pursuits. These establishments provide

sufficient night life in the shape of restaurants, bars, karaoke lounges and night clubs,

as well as day time activities specially designed around the type of people that

frequent the resort to encourage an extended, self-contained, on–site holiday. The

decision to approach these establishments to administer the survey for the study was

considered to be the best option to capture a wide range of resort users.

Industry associations such as the Australian Hotel Association, hospitality chains,

Holiday Inn Hotels and resorts and Best Western Hotels and various tourism boards

of different states and territories were approached. An internet search was also

conducted to identify and obtain contact details of a fair distribution in terms of

location, attractions, activities and physical amenities. In total, 348 different resort

establishments were identified for the study as follows.

S. No

Name of the State

Location of Resorts-(Mountain, Lake, Coastal, Wilderness etc)

Ski, Beach and Outback

No of Resorts 68

1. Victoria

South Australia Beach 22 2.

River, Beach and Lakeside 38 3. Western Australia

125 Rainforest, Beach and Outback 4. Queensland

Tasmania Beach and Ecological 14 5.

Heritage, Outback, Wild Life Reserve 10 6. Northern Territory

and Ecological

71 Ski, Beach and Outback 7. New South Wales

Total Australia wide 348

The largest concentration of resorts was found in Queensland with 125 properties,

followed by New South Wales 71, closely followed by Victoria with 68. Only 10

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properties could be identified as ideally suited for the study in the Northern Territory.

Australian Capital Territory was not included in the study as no hospitality

accommodation was classified as a resort there. Considering the nature of the

populace and the availability of attractions in the form of activities, which had already

been captured by research in other states close to Canberra, it was thought to negate

any perception of neglect and complacence.

A letter addressed to the owner / manager was sent to the postal addresses, followed

by a phone call to explain the aims and objects of the study and what was required of

them to help administer the survey. Many declined to participate out right on the basis

of either their fears about commercial confidentiality or lack of time and inclination.

After repeat telephone calls, 43 resort establishments agreed to participate in data

collection. They were each sent 50 questionnaires with postage paid envelops with

instructions to put the questionnaires in guestrooms and reception lobbies for a month

and send back the filled out ones after the said period. The sampling frame consisted

of the resort residents in the establishment. The restrictions of placing the self-

administered questionnaires in the guest rooms and the lobbies were a compromise as

the establishments did not want the researcher to approach their clients directly. This

arrangement suited the researcher as it allowed truly volunteer participation by the

resort users. After a month, 257 completed questionnaires were received. The contact

persons in the resorts were again called by phone to have the rest of the questionnaires

completed and they were given a further two weeks. Another 178 questionnaires were

received. Out of the total 435 questionnaires received, 23 were found to be unusable

due to being incomplete thus bringing the usable instruments tally to a final 412. The

final completion rate was 20.23% with 19.16% usability up from 11.95% after the

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first round. There are three possible reasons for the low participation rate. One, the

people who go to a resort look for peace, quiet and relaxation and there could have

been respondent apathy as this could have been perceived as an unwelcome chore.

Secondly, the guests may have been busy in activities outside their rooms to have

reasonable time to engage in the survey. Finally, the resort operators themselves may

not have encouraged the guests to participate in the survey sufficiently lest it should

be seen as disturbing the guests’ pursuit of leisure.

3.4.0 Sample Size:

There are many different approaches to decide on the sample size. Newman (2003)

suggests a principle that the smaller the population, the bigger the sampling ratio has

to be for an accurate sample. Larger populations permit smaller sampling ratios for

equally good samples. This is because as the population size grows, the returns in

accuracy for sample size shrink. Zikmund (2000) provides a sample size of 322 for a

population size of 500,000 - ¥ (the most appropriate band to reflect ABS figures)

with + 5% reliability. Burns and Bush (1995) provide a different approach for the

basic sample size where they consider three factors; The amount of variability

believed to be in the population, the desired accuracy and the level of confidence

required in the estimates of the population values. To obtain 95% accuracy at 95%

confidence level, the formula is

n = Z2 (pq)/e2

= 1.952 (0.5*0.5) / 0.052

= 385

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Where, n = sample size, Z = standard error associated with chosen level of confidence

(95%), p = estimated variability in the population 50% (The amount of variability in

the population is estimated to be 50%, a figure widely used in social research. From a

practical standpoint, most researchers choose the 50% level of p because it results in

the most conservative sample size according to Burns and Bush, 1995), q = (100 – p)

and e = acceptable error + 5%. Based on this formula there should be 385 data sets to

achieve a desired confidence level and accuracy.

Zikmund (2000) also suggests getting to a sample size on the basis of judgement.

“Using sample size similar to the sample size used in the previous studies provides the

inexperienced researcher with a comparison of other researchers’ judgement”.

Following is a table of tourism segmentation studies in recent times:

Researchers and Sample Sizes

Range < 100 101-200 Studies 1 4

201-300 5

301-400 14

401-500 3

501-600 5

601-700 3

701-800 4

Goulding (1999) – 33 Littrell and Song (2004) – 146; Kim, Wei and Ruys (1998) – 199; Mehmetoglu (2007) – 162; Peterson and Malhotra (2000) – 165 Chang (2006) – 215; Hu and Yu (2007) – 271; McCain and Ray (2003) – 220; Orth and Tureckova (2002) – 249; Trunfio, Petruzzellis and Nigro (2006) – 211 Baloglu, Weaver and McCleary (1998) – 314; Bigne and Andreu (2004) – 400; Charters and Ali-Knight (2002) – 368; Dolnicar (2005) – 373; Gonzalez and Bello (2002 – 400; Ibrahim and Gill (2005) – 400; Inbakaran and Jackson (2005) – 345; Lee, Morrison and O'Leary (2006) – 307; Leisen (2001) – 323; Liu (1999) – 387; Mok and Iverson (1999) – 319; Molera and Albaladeja (2006) – 335; Moskowitz and Krieger (2003) – 376 Beh and Bruyere (2007) – 465; Charters and Ali-Knight (2000) – 489; Prentice, Witt and Hamer (1998) – 403 Alvarez and Asugman (2006) – 503; Brown (2003) – 556; Kim and Jogarathnam (2003) – 514; Park, Yang, Lee, Jang and Stokowski (2002) – 523 Bloom (2005) – 634; Hong, Kim and Kim (2003) – 608; Mohsin (2005) – 670 Diaz-Perez, Bethencourt-Cejas and Alvarez-Gonzalez (2005) – 795; Frochot (2005) – 734; Inbakaran et al. (2005) – 776; Kim, Wei and Ruys (2003) – 720; McKercher and du Cros (2003) – 760 Baloglu and Uysal (1996) – 1,212; Dolnicar (2002) – 2,432; Dolnicar and Leisch (2004) – 5,365; Jang (2004) – 1,221; Juaneda and Sastre (1999) – 1,136

801-900 901-1000 > 1000 0 0 5

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The table clearly illustrates that the majority of the studies have been done on less

than 500 cases. In light of the above researchers in the tourism segmentation field, it

can be said with confidence that a sample size of 412 should be sufficient to segment

the resort tourist market.

3.4.1 Sample Size for Factor and Cluster Analyses:

The study employs factor and cluster analyses to segment the resort tourist market.

Literature does not provide a specific number of cases to conduct a factor analysis and

methodologists differ. There is near universal agreement that factor analysis is

inappropriate when sample size is below 50, preferable sample size should be 100 or

larger (Hair et al, 2006). However the following are arbitrary ‘rule of thumb’ numbers

proposed by various authors:

• Rule of 10. There should be at least 10 cases for each item in the instrument

being used (Hair et al, 2006).

• STV ratio. The subjects-to-variables ratio should be no fewer than 5 (Bryant

and Yarnold, 1995).

• Rule of 100. The number of subjects should be greater than 5 times the

number of variables, or 100. Even more subjects are needed when

communalities are low and / or few variables load on each factor (Hatcher,

1994).

• Rule of 150. 150 – 300 cases are recommended, more toward 150 end, when

there are few highly correlated variables, as would be the case when

collapsing highly multi-collinear variables (Hutcheson and Sofroniou, 1999).

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• Rule of 200. There should be at least 200 cases regardless of STV (Gorsuch,

1983).

• Rule of 300. There should be at least 300 cases (Norusis, 2005).

• Significance rule. There should be 51 more cases than the number of variables,

to support chi-square testing (Lawley and Maxwell, 1971).

There are 13 items and 60 variables in the instrument. According to the above, the

number of cases should be between the 111 and 300 band to be considered valid. The

study was done using 412 cases, a very statistically acceptable number.

Although the figure of > 1000 cases is historically recommended as clustering is

much less computer-intensive, Garson (2007) suggests hierarchical clustering with

sample size < 250, where as K-mean cluster analysis assumes a large sample size of >

200 (Kaufman and Rousseeuw, 1990).

3.4.2 Sample Size for Regression:

Cohen et al (2003) provided a sample size of 136 for a priori regression with alpha

level of 0.05, number of predicators 8, anticipated effect size (f2) of 0.15 being

medium, with a desired statistical power level of 0.8.

A rule of thumb for testing b coefficient is to have N > 50 + 8 m, where m = number

of independent variables (Tabachnick and Fidell, 2001). Another rule of thumb is that

there must be at least 20 times as many cases as independent variables. Garson (2007)

suggests that N>= 40*m rule of thumb since step wise regression methods can train to

________________________________________________________________ 55

noise too easily and not generalise in a smaller dataset. However, Stevens (1996)

recommends that for social science research, about 15 subjects for every predicator

are needed for a reliable equation.

3.5.0 Questionnaire Construction Approach:

The questionnaire was developed with the respondents’ perspective in mind. A pre-

testing was done to remove any confusion and ambiguous questions. As discussed

earlier the question of validity and reliability was considered so that the respondents

understand the questions and their replies are meaningful. The researcher is mindful

that the respondents are heterogenous in this research and it is impossible to have

questions that are equally clear, relevant and meaningful to all respondents. The

jargon, slangs and abbreviations were consciously avoided. Ambiguity, vagueness and

confusion were removed by passing it to the initial group of people. As the resorts

come at different levels of prestige, they were not mentioned as such to remove

prestige bias. There were no double-barrelled, double-negative, leading questions or

ones beyond the respondents’ capabilities. This was achieved by getting the

questionnaire proof read by the project supervisors who have many years of research

experience. The only criticism of the questionnaire that can be made is that it

requested answers to the questions pertaining to future intentions. This could not be

avoided as the study involves exploring intention under the TPB.

________________________________________________________________ 56

3.6.0 Ethics and Approval:

The study fell in the Risk Level 1 category as stipulated by RMIT as the study came

under the University’s definition of a ‘non invasive project where there is no apparent

risk to the participants beyond the everyday norm and where participants are not

identified’. An approval was sought from RMIT Business Portfolio Human Research

Ethics Sub Committee on the following basis:

• Subjects will not be identified and will thus remain anonymous.

Confidentiality of data will be maintained and only the investigator and

the supervisors will access the original data.

• The subjects will undertake no invasive procedures or experiments.

• There will be no manipulation of subjects with respondents receiving

clear instructions relating to the voluntary nature of the investigation

and the relevance of the research being undertaken.

• No subjects in this study will be asked to reveal any embarrassing,

confidential or compromising details. Questions asked will focus on

socio-demographics information and general characteristics such as

their beliefs and their intentions to use resort accommodation.

• The project does not involve the use of any equipment, which uses

electrical supply in any form e.g. Audiometer, biofeedback, electrical

stimulation etc.

• The project does not involve a fertilised human ovum or any samples

of body fluid or body tissue.

• None of the subjects will be finger printed or DNA ‘finger printed’.

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• None of the subjects are related or in any sort of dependent relationship

to the investigator.

• The project does not include the use of ‘n0-treatment’ or ‘placebo’

control conditions.

• The data will be held for five years.

The Business Portfolio Human Research Ethics Sub Committee required clarification

on the procedures to manage, monitor and report adverse and / or unforeseen events

relating to the collection, use or disclosure of information. The following reply was

given:

The participants have been informed of the following potential concerns and how they

are addressed, in the plain language statement letter accompanying the questionnaire.

1. Anonymity of the participants

a. The respondents will remain anonymous as they will not be identified

in any way.

b. The participants have the right to have any unprocessed data

withdrawn and destroyed, provided it can be reliably identified, and

provided that doing so does not increase the risk for the participant.

2. Pressure to participate in the study

a. The participants are randomly selected and they have an option to

withdraw at any time or refuse to participate in the study.

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b. The participant has the right to have any questions answered at any

time by calling the investigator or the supervisor.

c. The PLS that accompanies the questionnaire, also includes the Email

addresses of the investigator and the supervisor along with the address,

phone number and Email address of the Secretary, Human Research

Ethics Sub Committee, Business Portfolio, RMIT University in case a

participant wants to lodge a complaint or put a question directly in

writing.

3. Security of the survey material

a. The research data will be kept securely at RMIT for a period of 5 years

before being destroyed and will be seen by the researcher and his

supervisors only.

b. The research data will be coded by the investigator only.

4. Disclosure of information to the third party

a. The information that is provided in the questionnaire will be disclosed

only if (1) it is to protect the participant and others from harm, (2) a

court order is produced, or (3) the participant provides the researcher

with written permission.

The ethics approval was given on the basis of the above additional explanation.

3.7.0 Questionnaire Design and Construct:

The questionnaire was designed and constructed in the following manner.

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3.7.1 Plain Language Statement:

A plain language statement was created to accompany the questionnaire. This

statement was printed on RMIT University Business letterhead. It had the names and

contact details of both the investigator and the project supervisor. The letter was an

invitation to participate in the study. The investigators with their background and the

purpose of the study were explained. The aim of the study and the reason the person

was approached to participate was clearly written out. Furthermore, it explained what

the participant was required to do and any advantages or disadvantages associated

with participation. The anonymity and confidentiality related to the participation and

procedure in case the participant wanted to complain about any aspect of the study

was clearly provided in the letter.

3.7.2 Demographics:

The demographic questions were based on the study conducted by Inbakaran and

Jackson (2005) in resort hotels. The questions were based on gender (male / female),

age group (in blocks of 10 years, lowest ‘below 20’ and highest ‘61+’, education

(primary, secondary, TAFE or technical and university), marital status (single or

couple with or without children) and age of youngest child, occupation (professional,

clerical, managerial, unemployed, student and other), language (English or other),

resort visitation (first experience – years ago and average length of stay in number of

days).

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The primary reason for visiting a resort and the activities undertaken when at a resort

were also taken from the Inbakaran and Jackson (2005) study. The options for reasons

were scenic location, safety and security, recreational activities, range of available

accommodation, distance, information availability, ecology and surrounds, weather

and cost. The activities were horse riding, trekking, water based activities, sun

bathing, tennis, golf, meditation, skiing, bird and wild life watching, night life such as

clubs, bars or pub etc. and fishing. The options were tested on a seven-point Likert

scale with the lowest being extremely unimportant to the highest, extremely

important.

3.7.3 TPB:

The section dealing with the theory of planned behaviour was based on Ajzen’s

(1988, 1991, 2006) directions as to construction. Behavioural beliefs (BB), normative

beliefs (NB), control beliefs (CB), attitude toward the behaviour (AB), subjective

norm (SN), perceived behavioural control (PBC) and behavioural intention (BI) were

assessed directly by means of standard scaling procedures. The scales were developed

to be directly compatible with the behaviour in terms of action, target, context and

time elements. There were five questions each to test the items. The questions were

randomly dispersed in the section to get as honest response as possible. The set of

items used showed a high correlation with each other (the measure of high internal

consistency). Cronbach’s coefficient a in the acceptable level of 0.70 (Nunnally and

Bernstein, 1994).

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Methodologists simply use a rule-of-thumb that there must be a certain minimum

number of classes in ordinal categories (Achen, 1991), argues for at least 5, Berry

(1993) states five or fewer is ‘clearly inappropriate’. However, it must be noted that

use of 5-point Likert scales in regression is extremely common in the literature. For

the present study, all items were scaled on a seven-point Likert scale. This was done

as Nunnally (1978) recommended that

‘As the number of scale steps is increased from 2 up through 20, the

increase in reliability is very rapid at first. It tends to level off at about 7,

and after about 11 steps, there is little gain in reliability from increasing

the number of steps’.

3.7.3.1 Behavioural Intention:

Behavioural Intention (BI) of choosing a resort accommodation was measured by five

questions with a seven-point Likert scale, ranging from strongly agree (7) to strongly

disagree (1). The reliability analysis came up with final Cronbach’s a 0.871 after

removing 2 items. The final 3 items were ‘I intend to stay in a resort accommodation

in the forthcoming year’, ‘I will stay at a resort accommodation on my next vacation’

and ‘I plan to stay at a resort accommodation on my next holiday’.

3.7.3.2 Attitude toward the Behaviour:

Semantic differential was incorporated in the questions as they were dispersed in the

questionnaire. The bi-polar objectives were reached by the scaling from ‘strongly

agree’ to strongly disagree’ on the Likert scale. There were five questions reworded

around ‘my stay in a resort accommodation will be ……’ with variations of

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beneficial, pleasant, good, valuable and enjoyable. The acceptable level of Cronbach’s

a of 0.742 did not require removal of any items.

3.7.3.3 Behavioural Beliefs:

Behavioural beliefs (BB) consisted of two components: (a.) perceived likelihood of

outcomes of the behaviour, and (b.) evaluation of those outcomes. TPB assumes that

the belief strengths and outcome evaluations for the different accessible beliefs

provide substantive information about the attitudinal considerations that guide

people’s decisions to engage or not to engage in the behaviour under consideration.

Thus, the perceived likelihood and the outcome evaluation (OE) are multiplicatively

combined. A five items scale was developed with a seven-point Likert scale ranging

from strongly agree (7) to strongly disagree (1). A representative item of the BB was

‘It is fun to stay at a resort accommodation’ and that of the OE was, ‘Experiencing

resort experience is important’. The reliability analysis improved with the removal of

one item to 0.713. The final items in scale were ‘It is normal for people to stay in a

resort accommodation’, ‘It is fun to stay at a resort accommodation’, ‘It is safe to

stay in a resort accommodation’ and ‘It is convenient to stay in a resort

accommodation’.

3.7.3.4 Subjective Norm:

The social pressure one feels to conform to behaviour was measured by statements

such as, ‘most people who are important to me think that I should stay in a resort

accommodation’. Items such as the above have an injunctive quality, consistent with

the concept of subjective norm. Ajzen (2006) suggests that ‘responses to such items

are often found to have low variability because important others are generally

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perceived to approve of desirable behaviours and disapprove of undesirable

behaviours’. To remedy this problem, items were included questions designed to

capture descriptive norms i.e. whether important people to the subject themselves

perform the behaviour in question. This was addressed by the items, ‘most people

who are important to me use resort accommodation’ and ‘my friends tell me that it is

good to stay at a resort for vacation’. The reliability analysis achieved a final

Cronbach’s a of 0.768 after deleting two items. The final three items were, ‘Most

people who are important to me think that I should stay in a resort accommodation’,

‘My friends tell me that it is important to go to a resort hotel to have a good

vacation’, and ‘I am expected to go to and stay at a resort accommodation in future’.

3.7.3.5 Normative Beliefs:

The assessment of normative beliefs (NB) followed the logic similar to that involved

in the measurement of behavioural beliefs. It has two components (a.) normative

belief strength and, (b.) motivation to comply. The perceived normative pressure in a

given population is a measure of normative belief strength and motivation to comply

with respect to each referent. The representative items of NB were ‘my family thinks

that we should use a resort accommodation for our next vacation’, ‘the people in my

life whose opinion I value would approve of my resort accommodation choice’ and ‘I

will use a resort accommodation for a vacation if it was suggested to me by my

friends’. The reliability analysis came up with a low level of NB. Three items were

removed and Cronbach’s a of 0.677 was achieved after removing them. This was

lower than .7 but very close to the acceptable level. The final two items left on the

scale were ‘Most people who are important to me use resort accommodation’ and

‘The people in my life whose opinion I value stay in a resort accommodation’.

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3.7.3.6 Perceived Behavioural Control:

Five statements were used to measure perceived behavioural control (PCB), with a

seven-point Likert scale from strongly disagree (1) to strongly agree (7). The

reliability analysis showed a Cronbach’s a of 0.549 a poor reliability level. The

reliability did not improve significantly even after removing items so the scale was

left with all five items. They were, ‘If I wanted to, I could stay in a resort

accommodation in future’, ‘I believe I have a complete control over where I stay’, ‘I

always have fun at a resort because I want to’ and ‘It is mostly up to me to decide on

the type of accommodation I use’ and ‘I can refuse to go to a resort if I do not feel like

it’.

3.7.3.7 Control Beliefs:

Control beliefs (CB) consist of two components (a.) frequency of occurrence of the

facilitator or inhibitors of the behaviour, and (b.) perception of the strength of the

facilitator or inhibitors or power (P). Statements of these two components were again

multiplied and combined to obtain the overall level of CB. A sample of CB statements

was ‘using resort accommodation is expensive’ with a seven-point Likert scale from

strongly agree (7) to strongly disagree (1) and corresponding power (P) statement

was ‘the cost of resort accommodation would influence my buying decision’, again

ranging from strongly agree (7) to strongly disagree (1). The reliability analysis

came up with a Cronbach’s a of 0.391, showing no internal reliability. However, the

item ‘I mostly select resort accommodation to suite my needs’ strongly associated

with total behaviour and total intention, thus left in the scale.

________________________________________________________________ 65

3.8.0 Past Behaviour:

The questions on past behaviour were constructed based on Quellette and Wood

(1998). This was measured in two ways. One by asking the respondents the number of

times they have used resort accommodation in the past three years along with the

average length of stay with six frequency categories ‘less than 1 day’, ‘1-5 days’, ‘6-

10 days’, 11-15 days’, ‘16-20 days’ and ‘20+ days’. This was enriched with

statements such as ‘I feel comfortable going to a resort as I have experienced it before

and liked it’ and ‘I have taken my family / friends to a resort that I have been before’

on a seven-point Likert scale, ranging from strongly agree (7) to strongly disagree

(1). The reliability analysis showed the Cronbach’s a of 0.669, a level very close to

the acceptable level thus all five items were left in the scale. They were ‘I have

revisited a resort after being there with family or friends’, ‘I feel comfortable

revisiting resorts as they bring happy memories’ and ‘I have been to the same resort

hotel that I have visited with my parents when I was young’.

3.9.0 Data Screening:

Once the data has been coded and collected, it should be checked for errors to

maintain its accuracy and analysis prior to commencing analyses (Tabachnick and

Fidell, 1996). To ensure the accuracy of the data, all questionnaires were thoroughly

examined. Results from the questionnaires were then cross examined with the SPSS

data file to ensure that data entry had been completed without errors. Steven (1996)

has noted that there are many possible sources of data errors within research from the

________________________________________________________________ 66

initial data collection to the final coding and entry hence it is important that errors be

kept at a minimum. Scrutiny of questionnaires and use of SPSS removed the

possibility of such an occurrence.

3.10.0 Data Analysis:

The Statistical Package for Social Science (SPSS) was used for descriptive and

inferential analyses to provide respondents’ profile, correlations and Cronbach’s

reliability. Internal consistency and construct validity were performed by applying the

Cronbach’s a test and exploratory factor analysis respectively. In order to ascertain

whether tourists who buy resort accommodation are homogenous or not, this process

was employed. It is a common approach in tourism research to segment tourists into

clusters according to certain travel attributes. In this case, benefits and reasons for

travel they seek in a resort accommodation and test the first hypothesis.

Hypothesis 1:

Tourists using resort accommodation can be segmented in various

groups.

Benefit segmentation was introduced in a general marketing context by Hailey (1968)

as a technique for identifying market segments by casual factors, in order to provide a

better understanding of a particular market’s needs. It has been widely used in travel

and tourism research (Alvarez and Asugman, 2006; Balgolu et al, 1998; Beh and

Bruyere, 2007; Chang, 2006; Dolnicar, 2005; Frochot, 2005; Frochot and Morrison,

________________________________________________________________ 67

2000; Gonzalez and Bello, 2002; Hong et al. 2003; Hu and Yu, 2007; Jang et al,

2002; Kastenholz et al, 1999; Locker and Perdue, 1992; Sarigollu and Huang, 2005).

Cluster analysis was then used to search for relatively homogenous groups of shared

characteristics within given populations. Cluster analysis is a statistical technique that

places respondents into groups or clusters, so that those within each group are more

similar to each other than they are to members of other groups. Cluster analysis is

often used for its ability to produce a classification where there is little a priori

knowledge about the members of the categories that will be formed and who the

members of these categories will be (Churchill, 1995; Hair et al. 2006). Cluster

analysis has been used in numerous tourism studies and a review of literature reveals

that the method has been employed to segment tourists by activity (Lang et al., 1993),

participation (Morrison et al., 1995), perceptions (Fodness, 1990; Roehl and

Fesenmaier, 1992), benefits sought (Locker and Purdue1992), emotions (Bigne and

Andreu, 2004), experience patterns (Hull et al. 1992), equestrian activities (Brown,

2003), green tourism (Hong et al. 2003), motivation (Loker-Murphy, 1996),

information search strategies (Alvarez and Asugman, 2006; Fodness and Murray,

1998), rural tourism (Frochot, 2005), tourist role typology (Mo et al. 1994), wild life

reserve visit (Beh and Bruyere, 2007) and wine tourism (Charters and Ali-Knight,

2002). For this study, the 20 variables used came from the questions that asked

respondents to indicate how much influence a list of certain factors had on their resort

selection decision.

A K-means method of cluster classification was employed to manipulate the resort

selection data and a series of solutions ranging from two clusters to five clusters.

________________________________________________________________ 68

Ultimately four clusters were created and examined. Additional analysis, in the form

of discriminate analysis, which essentially tests the accuracy of the cluster

classification by estimating likelihood, was conducted to determine which cluster

solution provided the most useful market segments.

Regression analysis was used to test the following hypotheses:

Hypothesis 2:

Positive attitude toward resorts by the tourists is directly linked to

their accommodation purchase behaviour intention.

Hypothesis 3:

Positive subjective norm toward resorts by the tourists is directly

linked to their accommodation purchase behaviour intention.

Hypothesis 4:

Positive perceived behavioural control by the tourists toward resorts

is directly linked to their accommodation purchase behaviour

intention.

Hypothesis 5:

Past experience of resort accommodation purchase by the tourists

positively affects their behaviour intention.

________________________________________________________________ 69

Regression was employed to test the hypotheses as it is employed to predict the

variance in an interval dependent, based on linear regression. Its utility has been

proven many times in the fields of biology, behavioural and social sciences to

describe relationships between variables. Regression has been used for predictions –

that include forecasting of time series data, inference, modelling of casual

relationships and hypothesis testing. While Berk (2004) and Freedman (2005) have

criticised the procedure as it could be misused to uphold the assumptions that can not

be appropriately verified. In this case, the TPB has a valid foundation and its

assumptions have been tested many times. Therefore, the criticism is not applicable in

this case.

________________________________________________________________ 70

Chapter 4 Data Analysis

________________________________________________________________ 71

Chapter Overview _____________________________________________________

This chapter provides the results of data analysis to be used and discussed in

the next chapter as well as hypothesis testing. First, descriptive statistics are

reported from a respondent profile. Then the correlations among the

constructs are looked at. A regression analysis is conducted on the original

TPB model. This is done to test H2, H3 and H4. Furthermore, another

regression analysis is conducted on the proposed model that incorporates

TPB elements and past experience to see if behaviour intention could be

explained better. This is done to test H5. A cluster analysis is conducted to

create segments of the respondents using demographics and is explained.

Cluster differences are investigated using post-hoc analysis based on their

reasons, activities, beliefs and past experience. This tests H1. Finally

individual clusters are tested on the proposed model to discuss implications

of this study in the next chapter.

________________________________________________________________ 72

4.1.0 Sample Profile

This section provides an overall profile of the sample using descriptive statistics in

terms of demographic characteristics and resort usage status.

Table 4.1: Frequency Analysis of Respondents’ Demographic Characteristics

Demographic Variable

Frequency Percent 198 214 139 221 52 52 111 249 152 108 152 73 47 56 236 82 123 5 140 31 31 402 10 358 54 364 47 39 77 95 64 65 72 167 231 14

48.1 51.9 33.7 53.6 12.6 12.6 26.9 60.4 36.9 26.2 36.9 17.7 11.4 13.6 57.3 19.9 29.9 1.2 34.0 7.5 7.5 97.6 2.4 86.9 13.1 88.6 11.4 9.5 18.7 23.1 15.5 15.8 17.5 40.5 56.1 3.4

*TAFE – Technical and Further Education

________________________________________________________________ 73

4.1.1 Gender

The sample represented a male population of 48.1% to a female population of 51.9%.

This is very close to the reality as the national figure is 0.99 male(s) / female (2003

est.) (http://esa.un.org/nnpp/index.asp?panel=2).

4.1.2 Age Group

The data demonstrated the population percentage of people under 30 years old as

33.7%, very close to the national average of 42.3%. The 31 to 50 year olds bracket

came up with 53.6% observed population which is in reality only 29.7% of society. A

similar anomaly was observed for the 51 and above age group as represented in the

observed population. It came to 12.6%, while, it is 27.9% in the wider population

(http://www.ms.unimelb.edu.au/~moshe/moshe/australia.html).

4.1.3 Education Level

The largest group to be represented in the sample was university educated on 60.4%, a

figure more than double the country’s real number of 23.2%. Similarly, the people

who responded as having secondary education were under represented on 12.6%

when the actual number of secondary educated people is 42.6% (ABS, 2006)

(http://jobsearch.gov.au/training/default.aspx?pageld=information#EduPro).

4.1.4 Family Status

The large percent of the population indicated that they are either couples or couples

with children. This 63.1% representation is much higher than the national figure of

42.7%. Singles were also over represented at 36.9% as compared to the national

figure of 28.9% (ABS, 2003).

________________________________________________________________ 74

(http://www.abs.gov.au/websitedbs/D3110127.NSF/85255e31005a1918852558ac006

97645/2fff0f9d836819c4ca25694c00809a6a!OpenDocument)

4.1.5 Age of Youngest Child

The data under this category represented some interesting facts. The group who

indicated that there were no children living with them were the largest at 57.3%,

where as the people who indicated that there is a child of any age living in the

household came to 42.7%. According to ABS (2003), although in Australian families

with both dependent and independent children make up to 60%, only 45% of families

have at least one child aged 0–17 years. The sample was a very close representation of

the society at large (www.abs.gov.au/4442.0).

4.1.6 Occupation Category

Almost half of the sample was made up of managers, administrators and professionals

at 49.8%. This group was closely followed by a significantly large group comprising

of clerical, sales and service workers at 34.0%. A quick cross tabulation of this group

with sex showed that it was made up predominantly by women.

4.1.7 Australian Residency Status

The sample was predominantly made up of Australian residents.

4.1.8 Language Spoken at Home

The majority of respondents indicated that they speak English at home.

________________________________________________________________ 75

4.1.9 Resort Visitation in the last Three Years

This section showed that most of the people visited resorts less than 5 times over the

past three years.

4.1.10 First Resort Experience (History)

The response to this question elicited a well dispersed response showing almost equal

numbers in each category. The smallest group was made up of the people who said

that their first resort experience took place less than 1 year ago at 9.5%.

4.1.11 Usual Length of Stay

The responses indicated that predominantly, people stay at a resort for 1-2 weeks or

less.

4.2.0 Investigating the correlations between beliefs constructs

In the previous chapter, the various constructs of beliefs were checked for internal

consistency. Cronbach’s coefficient a in the acceptable level of 0.70 was considered

while deciding to keep the particular question or discard it. After collection and

tabulation of the questionnaires, the high ranking items were added together to get a

total item. A Pearson’s correlation analysis was run using SPSS to check their

multiple relationships. This was done to find the strength of relationships in the model

so that the model proposed by Ajzen (1991) could be tested and the addition of the

past experience could be made. Once the desired model was achieved, it could be

used to see if it is strong by running regression analysis.

________________________________________________________________ 76

The matrix (Table 4.2) showed some very interesting relationships. Every item

showed a significant relationship (p < 0.01 level, 2-tailed) with another, except where

perceived control and subjective norms were concerned. In their case, the Pearson

Correlation was at 0.066 with 2-tail significance at 0.182. Perceived behavioural

control correlated relatively low with intention and the behaviour as well, being 0.172

and 0.192 respectively. This relationship anomaly has already been noticed by Ajzen

(1993) as he depicts this in the original TPB model in the form of a dotted line.

The first model considered appeared as in (Figure 4.1). In the model there was a

strong link between attitude and intention that was observed to predict the behaviour.

The proposed past experience variable also came high in significance which gave

credence to a proposed addition to the TPB model. The perceived behaviour control

variable came at the lowest and its correlations with other elements were also not as

significant.

Figure 4.1: Correlations in the TPB Model

.625

.738

Attitude

.494

.586

.066

.372

.392

.5.799 Intention Behaviour Subjective Norms

Perceived Behaviour Control

________________________________________________________________ 77

Correlations

Table 4.2: Correlations

Total intention

Total Behav

Total Attitude Total Sub Norms Total Cont Bel

Total Perc Cont

Total Past Exp

Total Norm Bel

Total Intention

Pearson Correlation

Sig. (2-tailed)

412

Total Behav

Pearson Correlation

.799(**)

Sig. (2-tailed)

.000

412

Total Attitude

Pearson Correlation

.737(**)

.781(**)

Sig. (2-tailed)

.000

412

Total Sub Norms

Pearson Correlation

.586(**)

.488(**)

.625(**)

Sig. (2-tailed)

.000

412

Total Cont Bel

Pearson Correlation

.567(**)

.765(**)

.619(**)

.666(**)

Sig. (2-tailed)

.000

412

Total Perc Cont

Pearson Correlation

.066

.574(**)

.372(**)

.392(**)

.494(**)

Sig. (2-tailed)

.000

.182

.000

412

Total Past Exp

Pearson Correlation

.440(**)

.638(**)

.565(**)

.536(**)

.539(**)

.630(**)

Sig. (2-tailed)

.000

412

Total Norm Bel

Pearson Correlation

.475(**)

.529(**)

.512(**)

.624(**)

.665(**)

.562(**)

.470(**)

Sig. (2-tailed)

.000

.001

.000

412

** Correlation is significant at the 0.01 level (2-tailed).

________________________________________________________________ 78

The testing of the variables was conducted next using statistical process and

multiple regression to find out the equation that represents the best prediction of

a dependent variable, in this case intention, from several independent variables,

for example, past experience, attitude, subjective norms and perceived

behavioural control. The assumption that the variables relate to each other came

from the work of Ajzen (1991) and his established model. The choice of

regression was made primarily as the variables were of a continuous nature. The

secondary objective was to run a few models and come up with the best model

that predicts resort purchase behaviour.

Table 4.3 (a)

Model Summary(b)

Step 1: Regression Behaviour on Intention and Perceived Behaviour Control

R Square

Adjusted R Square

Std. Error of the Estimate

Model 1

.606(a)

.367

.364

2.68517

ANOVA(b)

a Predictors: (Constant), TotalPercCont, TotalIntention b Dependent Variable: TotalBehav

Mean Square

Sig.

Model 1

Regression

Sum of Squares 1710.981

855.490

118.651

.000(a)

Residual

2948.951

409

7.210

Total

4659.932

411

Coefficients(a)

a Predictors: (Constant), TotalPercCont, TotalIntention b Dependent Variable: TotalBehav

Sig.

Unstandardized Coefficients

Standardized Coefficients

Model

Std. Error

Beta

Std. Error

(Constant)

4.583

.492

9.307

.000

TotalIntention

.502

.034

.584

14.612

.000

TotalPercCont

.088

.038

.091

2.286

.023

a Dependent Variable: TotalBehav

The second regression analysis was done on TPB model as in Figure 5.1.

Figure 4.2:

Model Depicting Relationships past Intention

Attitude

Intention Subjective Norms

Perceived Behaviour Control

Table 4.3 (b):

Step 2: TPB Second Step Model Regression

Model Summary

R Square

Adjusted R Square

Std. Error of the Estimate

Model 1

.565(a)

.314

3.24133

ANOVA(b)

.319 a Predictors: (Constant), TotalSubNorms, TotalPercCont, TotalAttitude

Mean Square

Sig.

Sum of Squares

Model 1

Regression

2009.090

669.697

63.743

.000(a)

Residual

4286.548

408

10.506

Total

411

Coefficients(a)

6295.638 a: Predictors: (Constant), TotalSubNorms, TotalPercCont, TotalAttitude b: Dependent Variable: TotalIntention

Sig.

Unstandardized Coefficients

Standardized Coefficients

Model

Std. Error

Beta

Std. Error

(Constant)

.286

.726

.393

.694

TotalPercCont

.032

.048

.028

.665

.507

TotalAttitude

.435

.046

.445

9.433

.000

TotalSubNorms

.189

.044

.195

4.309

.000

a: Dependent Variable: TotalIntention Regression Intention on Perceived Behaviour Control, Attitude and Subjective

Norms:

1. Significant at 0.0001 with [R2 = 31.9%] variance explained

2. Attitude contributes 44.5%, followed by Subjective Norms at 19.5%,

followed by Perceived Behaviour Control, which is not significant and

only contributes 2.8%.

The regression explained the model to 31.9%. Past experience was added to this

TPB model as shown in Figure 4.3. The regression results are shown in Table

4.3.

Figure 4.3:

Correlations in the Proposed Model

.536

Past Experience

.638

.539

.781

.625

Attitude

.440

.586

.066

.799

.494

Behaviour Subjective Norms

.378

Intention

Perceived Behaviour Control

The model appears as shown in Figure 4.5 where correlations are shown. The

model also takes into consideration the relative strengths of correlations after the

reorganisation of the elements. A stepwise regression analysis was conducted

again in different stages. The first one was done on behaviour with attitude and

intention as shown in Figure 4.5.

Figure 4.4 (a):

Step 1: First Step of the Proposed Model

Attitude

Behaviour

Intention

Model Summary

R Square

Adjusted R Square

Std. Error of the Estimate

Model 1

R .674(a)

.454

.451

2.49424

Table 4.4: First Step Regression in Proposed Model

ANOVA(b)

a: Predictors: (Constant), TotalIntention, TotalAttitude

Model

Mean Square

Sig.

Sum of Squares

Regression

2115.449

1057.725

170.019

.000(a)

Residual

2544.483

409

6.221

Total

4659.932

411

Coefficients(a)

a: Predictors: (Constant), TotalIntention, TotalAttitude b : Dependent Variable: TotalBehav

Sig.

Unstandardied Coefficients

Standardized Coefficients

Model

Std. Error

Beta

Std. Error

(Constant)

2.863

.426

6.716

.000

TotalAttitude

.306

.036

.365

8.430

.000

TotalIntention

.347

.037

.403

9.316

.000

a : Dependent Variable: TotalBehav

Regression Behaviour on Attitude and Intention:

1. Significant at 0.0001 with [R2 = 45.4%] variance explained.

2. Intention contributes 40.3% while Attitude, another 38.5%.

Figure 4.4 (b):

Step 2: Second Step of Proposed Model

Past Experience

Attitude Subjective Norms

Perceived Behaviour Control

Table 4.5 Regression Second Step of Proposed Model (a)

Model Summary

R Square

Adjusted R Square

Std. Error of the Estimate

Model 1

.534(a)

.280

ANOVA(b)

3.40309 .286 a : Predictors: (Constant), TotalPercCont, TotalSubNorms, TotalPastExp

Sum of Squares

Mean Square

Sig.

Model 1

Regression

1888.777

629.592

54.364

.000(a)

Residual

4725.058

408

11.581

Total

411

Coefficients(a)

6613.835 a: Predictors: (Constant), TotalPercCont, TotalSubNorms, TotalPastExp b: Dependent Variable: TotalAttitude

Sig.

Unstandardized Coefficients

Standardized Coefficients

Std. Error

Beta

Model 1

(Constant)

3.447

.796

4.328

Std. Error .000

TotalSubNorms

.361

.043

.363

8.428

.000

TotalPastExp

.147

.033

.195

4.405

.000

TotalPercCont

.255

.049

.223

5.185

.000

a Dependent Variable: TotalAttitude Regression Attitude on Perceived Control, Subjective Norms and Past

Behaviour:

1. Significant at 0.0001 with [R2 = 28.6%] variance explained.

2. Subjective Norms contribute 36.3%, followed by Perceived Control

22.3%, followed by Past Experience 19.5%.

Figure 4.4 (c):

Step 3: Third Step of Proposed Model

Past Experience

Intention Subjective Norms

Perceived Behaviour Control

Table 4.6:

Regression Second Step of Proposed Model (b)

Model Summary

R Square

Adjusted R Square

Std. Error of the Estimate

Model 1

R .529(a)

.280

.275

3.33301

ANOVA(b)

a: Predictors: (Constant), TotalPercCont, TotalSubNorms, TotalPastExp

Model

Mean Square

Sig.

Sum of Squares

Regression

52.906

.000(a)

1763.182

587.727

Residual

4532.456

408

11.109

Total

411

Coefficients(a)

6295.638 a : Predictors: (Constant), TotalPercCont, TotalSubNorms, TotalPastExp b : Dependent Variable: TotalIntention

Sig.

Unstandardized Coefficients

Standardized Coefficients

Model

Std. Error

Beta

Std. Error

(Constant)

-.127

.780

-.163

.871

TotalSubNorms

.289

.042

.298

6.877

.000

TotalPastExp

.257

.033

.350

7.875

.000

TotalPercCont

.077

.048

.069

1.588

.113

a : Dependent Variable: TotalIntention Regression Intention on Past Experience, Subjective Norm and Perceived

Control:

1. Significant at 0.0001 with [R2 = 28.0%] variance explained

2. Past Experience contributes 35%, followed by Subjective Norms at

29.8%, followed by Perceived Control, which is not significant and only

contributes 6.9%.

Model 2 is the best option available as it makes further improvement in bringing

the regression predictability to 45.4% at 0.0001 significance, an improvement on

the original TPB model, by 13.5%.

Table 4.7:

Final Clusters by Demographics

Cluster

Gender

Age group

Education level

Family status

Age of the youngest child

Occupation

In the last three years, I have been to resorts ( ) times.

I have been using resorts for ( ) years.

My usual length of stay at a resort is

4.3.0 Rational for above variables

The reason for choosing the demographic factors such as gender, age group and

education level was to create a physically identifiable profile. Family status, age

of the youngest child living with the respondent and their occupation were

chosen to investigate if the variables predicted any disposable income,

availability of time and willingness to indulge in such activities. The last three

items were used to see the familiarity with resort visitation and their frequency

and length of stay.

Table 4.8:

Number of Cases in each Cluster

Cluster

63.000

134.000

142.000

72.000

Valid

411.000

Missing

1.000

Table 4.9:

Clusters Explained

Clusters by Demographics

Attribute

52.2%

61.3%

25.4%

47.9% N=197

Population Cluster 1 Cluster 2 Cluster 3 Cluster 4 33.3%

Gender (% males) N = 411 Chi-squ = 30.08 (df = 3) p <0.0001 Age

Under 20

5.1%

14.3%

9.0%

0.0%

7.0%

8.3%

36.5%

58.2%

23.8%

28.4%

9.7%

43.7%

25.4%

4.5%

40.8%

26.4%

0.0%

7.0%

26.4%

21-30 years 31-40 years 41-50 years 51-60 years 60+

0.0%

1.4%

29.2%

N=21 28.5% N=117 29.7% N=122 24.1% N=99 7.1% N=29 5.6% N=23

N=411 Chi-squ = 298.38 (df = 3) p <0.0001 Education Level

0.0%

1.4%

20.6%

9.7%

4.2%

26.4%

38.1%

8.5%

31.9%

39.7%

52.2%

87.3%

40.3%

Primary Secondary TAFE or Technical University

0.2% N = 1 12.4% N = 51 27.0% N = 111 60.3% N = 248

N=411 Chi-squ = 84.35 (df = 9) p <0.0001 Family Status

3.2%

6.3%

19.4%

82.8%

17.5%

0.0%

2.8%

0.0%

34.9%

17.2%

16.9%

54.2%

0.0%

26.4%

Single Single with Children Couple Couple with Children

44.4%

73.9%

33.1% N = 136 3.6% N = 15 26.3% N = 108 37.0% N = 152

N = 411 Chi-squ = 329.95 (df = 9) p <0.0001 Age of Youngest Child

1.5%

0.0%

38.1%

33.1%

20.6%

0.0%

23.2%

1.4%

12.7%

2.2%

20.4%

22.2%

Less than 6 years Between 6-15 years Older than 15 None

28.6%

23.2%

96.3%

76.4%

17.8% N = 73 11.4% N = 47 13.6% N = 56 57.2% N = 235

N = 411 Chi-squ = 209.46 (df = 9) p <0.0001 Occupation Category

0.0%

11.9%

1.4%

45.8%

3.2%

9.7%

34.3%

47.2%

Managers and Administrators Professionals Labourer and Related Workers

20.0% N = 82 29.7% N = 122 1.2%

0.0%

1.5%

0.7%

2.8%

6.3%

71.4%

35.1%

54.2%

11.1%

17.2%

0.0%

1.4%

Clerical, Sales and Service Workers Students Others

14.3%

0.0%

30.6%

N = 5 34.1% N = 140 7.5% N = 31 7.5% N = 31

N = 411 Chi-squ = 293.03 (df = 15) P<0.0001 Resort Visitation in last 3 years

85.7%

88.1%

91.5%

86.1%

11.1%

9.7%

6.3%

13.9%

3.2%

2.2%

2.1%

0.0%

88.6% N = 364 9.5% N = 39 1.9% N = 8

< 5 times 6-10 times > 10 times

N = 411 Chi-squ = 5.35 (df = 6) p<0.499 History of Resort Visitation

12.6%

21.6%

1.4%

0.0%

2.8%

34.9%

35.8%

26.9%

21.1%

5.6%

39.7%

11.1%

10.4%

19.0%

22.2%

0.0%

5.2%

15.3%

33.1%

1.6%

0.0%

22.5%

54.2%

First visit was less than 1 year ago 1 - 5 years ago 6 - 10 years ago 11 - 15 years ago 16 - 20 years ago 20+ years ago

9.5% N = 39 18.5% N = 76 23.1% N = 95 15.6% N = 64 15.8% N = 65 17.5% N = 72

N = 411 Chi-squ = 258.06 (df = 15) P <0.0001 Usual Length of Stay

0.0%

0.7%

0.0%

33.3%

20.8%

50.0%

43.7%

65.1%

42.5%

54.9%

76.4%

1.6%

3.7%

1.4%

1 day Less than a week 1 - 2 weeks 2 - 3 weeks 3+ weeks

0.0%

3.0%

0.0%

1.4%

0.2% N = 1 40.1% N =165 56.2% N = 231 2.2% N = 9 1.2% N = 5

N = 411 Chi-squ = 31.88 (df = 12) p < 0.001

4.4.0 Clusters Description

The clusters created by K-means clustering technique created 4 healthy sized

clusters based on demographics. The following is a short description of the

clusters:

4.4.1 Cluster 1 (Active Conventionalists)

This cluster comprised of a predominantly female population of 21-30 years of

age (36.5%). There were some small but significant other age groups that were

sorted into an age group of 31-50 years, representing 47.9% of the cluster. Their

education level was equally dispersed between TAFE or technical (39.7%) and

University (39.7%). Most of the group members were a couple with a child

(44.4%). Majority of them had a child below 6 years of age (38.1%), while a

smaller number of people said that they did not have a child (28.6%). An

overwhelming number of people were working in clerical, sales and service

positions (71.4%). Most (85.7%) of the cluster members have been to a resort

less than 5 times in the last three years. The majority (74.6%) had visited a resort

for the first time less than 10 years ago. Their usual length of stay at a resort was

1-2 weeks (65.1%).

4.4.2 Cluster 2 (Young Conservatives)

This group was gender balanced, made up of males (52.2%) of between 21-30

years (58.2%). They were predominantly university educated (52.2%). They

were single (82.8%) with no children (96.3%). They were employed as

professionals (34.3%) or in the service industry (35.1%). As in Cluster 1, they

had visited resorts less than 5 times in the last 3 years (88.1%). However, unlike

the members of the above cluster, they had a shorter first time resort visitation 1-

5 years ago (35.8%). Another distinguishing feature was that most (50.0%)

stayed at a resort for less than a week while a significant number of people

(42.5%) stayed for 1-2 weeks at a resort.

4.4.3 Cluster 3 (Elite Regulars)

This cluster had older, 31-50 year old (84.5%) males (61.3%) who were

university (87.3%) educated. Most of the members (73.9%) indicated that they

were a couple however the age of their youngest child was varied with the largest

(33.1%) indicating that the age of the youngest child is below 6 years. Of all the

groups, this one indicated the highest employment category with 45.8% as

managers and administrators and 47.2% as professionals. They were similar in

their resort visitation at less than 5 times in the last 3 years (91.5%) like other

clusters. The majority (55.6%) had experienced a resort for the first time 16 years

or more ago. While the majority (54.9%) stayed at a resort for 1-2 weeks, a

substantial number (43.7%) indicated a shorter stay at less than a week.

4.4.4 Cluster 4 (Veterans)

This cluster was made up of mature 41+ year old (82.0%) females (66.7%).

Although their education levels differed, from secondary (26.4%) and TAFE or

technical (31.9%), a significant number was university (40.3%) educated. A large

number (54.2%) were couples with no child (76.4%) living with them. Most

were employed in clerical, sales and service positions (54.2%), while a good

number of them indicated their employment as other (30.6%) that included home

duties. This cluster had the longest resort visitation history with 54.2% indicating

that they visited a resort over 20 + years ago for the first time and they stay for 1-

2 weeks (76.4%) at a resort when they visit one.

Table 4.10:

Clusters by Reasons, Activities, Past Experience and TPB

Factoring Variable

Mean Score for Each Cluster

Sample Mean

Cluster differences detected from post-hoc analysis

1

2

3

4

1.87

1.88

None

1.83

1.95

1.83

1.86

1 ≠ 3

1.83

1.56

1.94

2.59

2.4

2.58

4 ≠ 1, 2, 3

2.33

3.25

2.46

1 ≠ 2, 3

2.45

2.5

1.95

2.61

3.38

3.51

3.54

1 ≠ 2, 3, 4

1.44

3.57

2.07

2.04

None

2.03

2.11

2.33

3 ≠ 1, 4

2.66

2.72

2.32

2.91

2.2

2.31

3 ≠ 1, 2

1.86

2.51

None

1.58

1.5

1.67

1.41

1.69

5.42

None

5.28

5.15

4.9

5.48

4.34

4.51

4.3

None

4.52

3.9 Reasons Scenic Location F=.603 (df=3, 407) Not Significant Safety and Security F=3.908 (df=3, 407) p = 0.009 Recreational Activities F=12.855 (df=3, 407) p < 0.0001 Range of Accommodation F=5.861 (df=3, 407) p = 0.001 Distance from Home Town F=7.708 (df=3, 407) p < 0.0001 Available Information F=0.174 (df=3, 407) Not Significant Ecology and Surrounds F=6.720 (df=3, 407) p < 0.0001 Weather F=8.041 (df=3, 407) p < 0.0001 Cost F=3.036 (df=3, 407) p = 0.029 Activities Horse Riding F=2.597 (df=3, 407) p = 0.052 Trekking F=2.232 (df=3, 407) p = 0.084

2.82

2.81

2 ≠ 1, 3, 4

2.64

2.23

2.9

3.14

3.07

3.14

None

3.1

3.22

3.85

3.76

3.87

None

4.16

3.67

4.09

4.26

3.92

None

4.48

3.75

4.29

4.3

3 ≠ 4

4.63

3.6

4.31

4.35

4.31

1 ≠ 2

4.84

3.99

4.13

4.1

4.18

None

4.26

3.88

3.64

3.27

4.14

1 ≠ 3, 4 2 ≠ 3, 4

2.92

4.25

5.13

2 ≠ 3

4.93

5.11

4.51

5.15

8.61

8.59

8.75

3 ≠ 4

7.75

10.14

4 ≠ 2, 3

9.88

10.19

9.36

9.16

11.46

13.14

13.52

1 ≠ 3, 4

11.56

14.07

11.98

11.56

12.24

3 ≠ 1, 2

10.41

12.98

2.29

2.32

2.54

3 ≠ 2, 4

1.84

2.69

11.09

11.28

10.83

None

10.08

11.52

17.27

17.42

17.33

None

16.61

17.5

6.85

6.63

7.16

None

6.27

7.22 Water Activities F=7.020 (df=3, 407) p < 0.0001 Sun Bathing F=0.163 (df=3, 407) p = 0.921 Tennis F=1.019 (df=3, 407) p = 0.384 Golf F=2.307 (df=3, 407) p = 0.076 Meditation F=4.710 (df=3, 407) p = 0.003 Skiing F=3.838 (df=3, 407) p = 0.010 Bird or Wild life Watching F=0.677 (df=3, 407) p = 0.567 Night Activities F=15.395 (df=3, 407) p < 0.0001 Fishing F=3.836 (df=3, 407) p = 0.010 Items from the Proposed Model Intention F=6.256 (df=3, 407) p < 0.0001 Behaviour F=8.128 (df=3, 407) p < 0.0001 Attitude F=5.190 (df=3, 407) p = 0.002 Subjective Norms F=6.948 (df=3, 407) p < 0.0001 Control Beliefs F=8.710 (df=3, 407) p < 0.0001 Perceived Control F=2.752 (df=3, 407) p = 0.042 Past Experience F=0.394 (df=3, 407) p = 0.757 Normative Behaviour F=2.547 (df=3, 407) p = 0.056

4.5.0 Cluster Description on Reasons, Activities, Past Experience

and TPB

4.5.1 Cluster 1

The cluster scored high on outdoor activities such as trekking, water based

sports, tennis, golf and skiing. They did not care about safety and security (unlike

Cluster 3), range of accommodation (unlike Cluster 2 and 3), distance to the

resort from home (unlike the rest of the clusters), ecology and surrounds (unlike

Cluster 3) and weather (unlike Cluster 3).

They scored lowest for attitude, subjective norms, perceived control, past

experience and normative beliefs.

4.5.2 Cluster 2

This cluster scored highest on scenic location of the resort, the distance to the

resort from their home town, available information about the resort, sun bathing

and wildlife and bird watching. This cluster was least interested in availability of

recreational activities (unlike Cluster 4), water activities (unlike Clusters 1, 3 and

4), skiing (unlike Cluster 1) and fishing (unlike Cluster 3).

They scored lowest on behaviour.

4.5.3 Cluster 3

This cluster scored highest on their need for safety and security, availability of

accommodation range, ecology and surrounds, weather, horse riding, meditation

and fishing. They were similar to Cluster 1 for their low interest in the scenic

location of the resort which is unlike Cluster 2.

They scored high on subjective norms and perceived control.

4.5.4 Cluster 4

This cluster scored highest in their need for the range of available recreational

activities, cost and night activities such as going to pubs and restaurants. They

were least interested in trekking (unlike Cluster 1), tennis (unlike Cluster 1), golf

(unlike Cluster 1), meditation (unlike Cluster 3) and wildlife watching (unlike

Cluster 2).

They had the highest scores for intention, behaviour, attitude, control beliefs,

past experience and normative behaviour.

Table 4.11:

Final Regression Cluster Number by Behaviour to Intention and Attitude

b b b b b b Significance R2 Attitude b Intention b

Cluster Number 1 2 3 4 .288 .536 .674 .808 P < 0.0001 P < 0.0001 P < 0.0001 P < 0.0001 .595 .356 .478 .478 -.139 .474 .457 .527

The regression Behaviour on Intention and Attitude was run individually and the

results achieved are displayed above in Table 4.11. First, the clusters were sorted

and saved as four different files. Once this was achieved, only one type of cluster

was allowed to remain on individual files. A regression analysis was conducted

on each file using Linear Regression with Mahalanbis distances option.

All clusters showed a high significance at 0.0001 with a varying degree of [R2]

variance explained. Cluster 4 was the most predictable at [R2 = 80.8%] variance

explained. In this Cluster the contribution of Attitude was the highest at 52.7%,

followed by Intention 47.8%. This is good as the cluster makes 17.5% of the total

population. In the case of Cluster 3, whose membership represented 34.6% of the

sample, the variance was 67.4% explained. For this Cluster, Intention contributes

higher at 47.8% followed by Attitude 45.7%. Cluster 3 represents 32.6% of the

total sample. A healthy variance [R2] was explained at 53.6%. In this case the

Attitude contributes at 47.4%, followed by Intention at 35.6%, similar to Cluster

4. The smallest number of sample population is represented in Cluster 1 at

15.3%. This cluster is the least predictable as the [R2] is only 28.8% explained.

Although Intention contributes a healthy 59.5% the Attitude is not significant at

–13.9%.

Chapter 5 Discussion and Implications

Chapter Overview _____________________________________________________

This chapter presents the summary of findings in relation to

segmentation of resort tourists: How the theory of planned

behaviour relates to predicting resort accommodation purchase

behaviour intentions along with the contribution of past

behaviour to the theory. Practical implications of the study are

discussed and recommendations are made. The major findings

reported in the previous chapter are summarised in the research

context of the research objectives at the outset. Out of the five

hypotheses proposed for the study, three were supported by the

data, one was partially supported and one was rejected.

5.1.0 Resort tourists segmentation: The four clusters identified in the research, active conventionalists, young

conservatives, elite regulars and veterans differ statistically in terms of gender

ratio, age, level of education, family status, occupation, frequency of visits, the

history of resorts visitation and usual length of resort stay. Each of these cluster

groups were then compared in terms of reasons for choosing a particular resort,

activities they indulge in while staying in a resort, past experience and items

specific to the theory of planned behaviour. The expanded cluster profiles can

now be described.

Resort tourists belonging to cluster one (N = 63 or 15.33% of the total

respondents), active conventionalists, are predominantly educated young women

with at least one child and in a relationship. Their main occupation is clerical,

sales or service. They visit a resort at least once a year and their first visit to a

resort was less than 10 years ago. The main activities they indulge in are very

action orientated such as trekking, swimming, tennis, golf and skiing. They are

not interested in horse riding, fishing or bird watching. This could mean that they

have children to look after and can only indulge in activities that take less time

away from looking after the children. In the same way they are moderately

interested in meditation and sun bathing. They are not very concerned about

safety and security or range of available accommodation. Interestingly, they are

also not effected by the distance it takes to travel to their resort destination. This

could mean that either they are not the designated drivers or they fly to the

destination, thus reducing the negativity associated with the distance to the resort

to a great degree in their responses. On the psychological front, their behaviour

in purchasing resort accommodation is least formed by their attitude toward it,

control belief, past experience or normative beliefs. This leads to the assumption

that they are not the primary deciders of the type of resort accommodation they

use. Over all, this segment seems to be interested in activities that do not take a

long period of time and focuses on the ones who accompany another person who

decides on the choice of the resort accommodation. This group resembled

‘romantics’ as proposed by Inbakaran and Jackson (2005) in their study of

Australian resort visitors.

The profile of the second cluster (N = 134 or 32.60%), focuses on young

conservatives, who as a group, seem to be very concerned about the scenic

location of the resort. They also consider the distance they have to travel to reach

a resort an important factor when deciding. They actively seek the information

about the resort they wish to decide on. They seem to be more interested in

sedentary life such as meditation, ecology and surrounds, sun bathing and wild /

bird life watching. They seemed moderately interested in tennis and golf. They

were least interested in availability and range of activities, night life, water

activities and skiing. This group represents the face that matches such

preferences. This group is an even mix of males and females in the 21 – 30 years

age range. They are predominantly university educated, single and with no

dependent children. They were least bothered about the cost. This group

indicated that they were either professionals or employed in clerical, sales or

service positions. The most distinguishing factor was that they had the shortest

length of stay at a resort. The group is very similar to the ‘immersers’ as

proposed by Inbakaran and Jackson (2005). This group seems to come from a

background of hectic work schedules as they are interested in relaxing and

100

indulging in low impact activities. Another possibility is that this group comes to

the resort for a conference and after their long day at work related activities they

want to rest. This assumption is based on the fact that their resort utilisation

history is the shortest along with the length of stay being less than a week.

The third segment (N = 142 or 34.55%), elite regulars, comprised mostly of

university educated males of the 31 – 50 years age group. The majority of them

were married or in a relationship with at least one child living with them. They

were the highest earners as this group represented managers / administrators or

professionals. They had their first resort stay experience more than 16 years or

more ago. While the majority stayed for 1 to 2 weeks at a resort, a substantial

number stayed there for less than a week. This short stay may indicate that they

may be at the resort for purposes other than mentioned in the questionnaire such

as attending a conference. Considering their age bracket it is not surprising that

they were concerned about safety and security. They also scored high on the

availability of the range of accommodation. While they scored moderately high

on distance from home, availability of information and cost, they scored highest

on their preference for ecology and surrounds and weather. Their preference for

the activities also set them apart as this group was most interested in horse riding,

meditation and fishing. They also scored moderately high on night life, bird and

wild life watching and trekking. On the basis of demographics this group

reflected similar attributes as the ‘tasters’ of Inbakaran and Jackson (2005). On

the beliefs side, this group scored high on subjective norms and perceived control

where as on intention and control beliefs they scored lowest. They scored

moderately high on past experience and attitude. The profile and the preferences

of the group provide a strong indication that they are interested in activities that

101

take them about and increase their interaction with other resort users.

The final segment (N = 72 or 17.52%), veterans, contained women over 41 years

of age who were university educated. Although they came from the whole

spectrum of education level, a significant number had university education,

followed by TAFE or technical that made almost one third of the membership

and finally, a quarter of the total indicated that they had secondary education. A

significant majority were in a relationship with no children living with them at

home. While over half of the respondents indicated that they were employed in

clerical, sales or service positions, a third indicated their employment as other

that included home duties. They had the longest history of resort visitation and

their stay length at a resort was for 1 – 2 weeks. Their preference was for the

availability of the recreational activities variety. This means that they participate

most in the activities that are packaged by the resorts. They also indicated that

the range of available accommodation along with the cost were important factors

in their choice formation. This does not come as a surprise as that age group is

generally price sensitive. The distance from home to the resort appeared as a

significant factor which could mean that they drive themselves to the resorts.

Good weather, scenic location that came with safety and security again correlated

well with the age group and the predominant gender of the segment. They liked

to socialise in the night activities by going to the restaurants and pubs. Regarding

the activities, they like to horse ride and sun bathe. Once again, this group

resembled the ‘veterans’ of Inbakaran and Jackson (2005). This group was found

to be very interesting in their beliefs and past behaviour set. They had the highest

behaviour, intention and attitude. They believed that they can control their

experience at a resort. Their past experience helped them form their choice. They

102

also conformed to the beliefs of their family and friends.

The post hoc analysis of the cluster differences provided some very interesting

insights. On their reasons for visiting a resort, every segment liked the resort to

be situated in a scenic location which may have some implications for the initial

resort development phase. There was no significant difference in the need for the

availability of information about the resort. Every segment was interested in

learning about the resort before embarking on the purchase. Cost was also an

important factor.

The activities the tourists indulge in also gave some interesting insights. There

was no significant difference between the groups on horse riding, trekking, sun

bathing, tennis, golf and bird and wild life watching. This discussion supports the

hypothesis (H1) that ‘tourists using resorts accommodation can be segmented in

various groups’.

5.2.0 Beliefs and Resort Tourist’s Accommodation Purchase

Behaviour:

The basic aim of this research is to explore the role of beliefs in resort tourists’

accommodation purchase behaviour. The seminal work in this field has been

done by Ajzen (1991) in proposing the theory of planned behaviour, a theory

used in consumer behaviour and psychology areas. According to Ajzen (1991)

human behaviour is guided by three kinds of considerations; beliefs about the

likely outcomes of the behaviour and evaluation of these outcomes, namely

behavioural beliefs which culminate in attitude, beliefs about the normative

expectations of others and motivations to comply with these expectations,

103

normative beliefs which form subjective norms and beliefs about the presence of

factors that may facilitate or impede performance of the behaviour and perceived

power of these factors and control beliefs which manifests as perceived

behavioural control.

Figure 5.1 in the previous chapter represents the correlations in the TPB model.

Although the TPB model has some detractors (Eagly and Chaiken, 1993),

intention was found to be a precursor to the behaviour (Fishbein and Ajzen,

1977, Ajzen, 1991 and Ajzen and Driver, 1992). The data analysis of the TPB

model on the available responses gave the correlation between behaviour and

intention with a of 0.799. The reliability Chronbach’s a showed a high level of

internal reliability being above 0.7 for some constructs as suggested to be

adequate by McGraw and Wong (1996) and above the conventionally lenient

cut-off point of a 0.6.

Present research is using TPB for the first time for the resort tourists therefore

there is no precedence to conform to or reject. Wherever possible, similarities are

highlighted in findings of this research and other researches done in other

disciplines.

The correlations in the original model showed a reliability a of 0.738 between

intention and attitude. Attitude refers to the degree to which a person has a

favourable or unfavourable approach to the behaviour intention in question. This

high level of reliability has been seen in the case of tourism for leisure choice

(Ajzen and Driver, 1992), travel mode (Bamberg et al. 2003), restaurant related

research in hospitality (Buttle and Bok, 1996; Conner et al., 2001; Lam and Hsu,

104

2004; Reisinger and Waryszak, 1994) as well as other disciplines to investigate

exercise (Dawns and Hauseblas, 2003), sun-related behaviour (Branstrom et al.,

2004) and promotion of whole-grain foods by dieticians (Chase et al. 2003).

While investigating regression intention on attitude it showed a significance of

0.0001 with variance explained [R2 = 31.9%], attitude contributed 44.5%. The

explained variance is low but considering there is no precedence for resort tourist

research, Rhodes et al (2005), while summarising previous research applying

TPB on exercise behaviour, suggested that TPB explained an average of 30% of

attitude and 40% variance in intention. Similarly, in another study on the variety

of health related behaviours the TPB explained 27% and 39% of the variance in

behaviour and intention respectively (Armitage and Conner, 2001).

This study’s regression explanation for attitude’s contribution is the same as

Ajzen and Driver (1991), 0.36 in relation to male and female undergraduate

students, Backman (1999) 0.47 in relation to diet and health, Bergen (1996) 0.45

in relation to chronic pain, Blue (1996) 0.52 in exercise and work site employees,

Cournya (1995) 0.44 in older adults and muscular activities, Daltroy and Godin

(1989) 0.39 in spouses of cardiac patients and improved lifestyle, Godin et al.

(1983) 0.42 in relation to barriers to healthy living, Kimiecik (1992) 0.34 for

worksite employees and Legg (1987) 0.26 for undergraduate students. The

positive relationship found between behaviour intention and attitude in this study

supports the hypothesis (H2) that ‘positive attitude toward resorts by the tourists

is directly linked to their accommodation purchase behaviour intention’.

The correlation between subjective norms and behaviour intentions was found to

be with reliability a of 0.586, lower than the conventional cut off point of 0.6,

105

but the proximity can not be discounted as it is very close to it and the literature

is full of results that confirm such a relationship. In Table 5.3 (b) regression

Intention on Subjective Norms a significance of 0.0001 with [R2 = 31.9%]

variance is explained. Subjective Norms contributed 19.5%. A similar trend

appeared while investigating the level of Subjective Norms in various clusters as

shown in Table 5.10. With the exception of Elite Regulars, every other cluster

showed a low mean score. This low relationship discovery partially supports the

hypothesis (H3) that ‘positive subjective norm toward resorts by the tourists is

directly linked to their accommodation purchase behaviour intention’.

In the case of Perceived Behaviour Control, the reliability a was found to be

lowest in relation to other items. For Behaviour it was 0.392; Intention, 0.372,

Subjective Norms, 0.066 and Attitude, 0.294 (Table 5.2). It also explained

Intention in Table 5.3 (b) to a very low 2.8%. In the original model, Perceived

Behaviour Control is said to be a direct predicator of Intention and an indirect

one of Behaviour. In a tourism related study of potential travellers from mainland

China to Hong Kong, Lam and Hsu (2004) found that Attitude and Perceived

Behaviour Control were related to travel Intentions, however in the present

study, Perceived Behaviour Control has been found to be low for all segments

and it did not contribute to their Behaviour Intention. This result does not support

the hypothesis (H4) that ‘positive perceived behavioural control by the tourists

toward resorts is directly linked to their accommodation purchase behaviour

intention’.

5.3.0 Past Experience and Resort Tourists’ Accommodation

Purchase Behaviour

106

Past experience was incorporated in the original TPB model. After a few

attempts, streamlining the model was achieved (Figure 5.3) when taking the

correlations into consideration. Past Experience correlated strongest to Intention

(a = 0.638), followed by Attitude (a =0.536), Perceived Behaviour Control

(a =0.440) and Subjective Norms (a =0.439). Regression analysis was conducted

on the model in three different steps. Step 1, was regression Behaviour on

Intention and Attitude, which was found to be significant at 0.0001 with [R2 =

45.4%] variance explained, an immediate improvement over the original model

that had [R2 = 31.9%] variance explained. Furthermore, in Step 2, regression

Attitude on Perceived Behaviour Control, Subjective Norms and Past Behaviour

showed significance at 0.0001 with [R2 = 28.6%] variance explained (Table 5.5).

In this case Subjective Norms contributed 36.3%, followed by Perceived

Behaviour Control 22.3%, followed by Past Experience 19.5%. In Step 3,

regression Intention on Perceived Behaviour Control, Subjective Norms and Past

Behaviour showed significance at 0.0001 with [R2 = 28.0%] variance explained.

In this case, Past Experience contributed 35%, followed by Subjective Norms

29.8%, followed by Perceived Control, which was not significant and only

contributed 6.9%. This result is similar to other travel related studies (Ajzen and

Driver, 1992; Lam and Hsu, 2006). This also validates that Past Behaviour has a

significant impact on Behavioural Intention (Lam and Hsu, 2006). This finding

also aligned with Ajzen’s (1991) argument that when people deliberately form

conscious intentions, past behaviours are likely to be a contributing factor.

Moreover, the finding supports empirical studies, which demonstrated that past

behaviour has a direct influence on behavioural intention of different types of

107

acting (Ajzen and Medden, 1986; Bentler and Speckart, 1979). This finding

supports the hypothesis (H5) that ‘past experience of resort accommodation

purchase by the tourists positively affects their behaviour intention’.

Table 5.1 Summary of Findings

Hypothesis resorts accommodation can be Results Supported H 1

Supported linked H 2

H 3 Partially Supported

Not Supported directly resorts linked is H 4

Supported Tourists using segmented in various groups. Positive attitude toward resorts by the tourists is directly their accommodation purchase to behaviour intention. Positive subjective norm toward resorts by the tourists is directly linked to their accommodation purchase behaviour intention. Positive perceived behavioural control by the tourists toward their to accommodation purchase behaviour intention. Past experience of resort accommodation purchase by the tourists positively affects their behaviour intention.

H 5

5.4.0 Implications

While studying the composition of various clustering groups for marketing

purposes, their reasons for utilising a resort accommodation for holiday purpose

will give a keen insight into the particular groups for the resort marketing team to

target. This is recommended because it is easier to match the segment to the

existing facilities than to opt for very expensive modifications to the physical and

structural attributes of the facility. At the time when resort management is

focusing on traditional and non-traditional markets, it is advisable that they

understand the underlying dimensions of resort visitation (Barsky, 1992;

Dannaher and Mattsson, 1994; Oh, 1999). Furthermore, the operators of those

108

resorts can offer the activities preferred by the target group at a reasonable cost to

make their stay an enjoyable and memorable one. Chon and Singh (1995) have

found that a changing client lifestyle, composition and background along with

spending patterns exert enormous market pressure for the resorts all over the

world. The growth of an evolving Chinese market is a good example in the

Australian context. Therefore, basing demographics as the clustering base and

using them to develop guest activity programs and other resort products and

services will have limited resource implications (Warnken et al., 2003).

The first cluster, ‘active conventionalists’ seems to represent young couples with

or without small children. They also seemed to be the ones who wanted to enjoy

their resort stay with minimal difficulties. While this cluster is the smallest, they

require a marketing strategy that focuses on the availability of relaxing surrounds

with basic facilities such as golf, tennis and swimming and proximity to

geography that meets their interest in skiing and trekking. Their need to indulge

in such activities seems to be hindered by their obligation to look after the small

children. It is recommended that the resorts interested in this segment should

invest in child minding facilities and promote such facilities so that these people

can indulge in their favoured activities, relax and re-patronise the resort as a

regular refuge. International market focused resorts could also market to the

lucrative Asian young and honeymooner market although there is a need to be

aware of the cross-cultural service issues (Turner et al., 2001; Wei et al., 1989).

As this segment is represented by young couples, the resort activity program

could focus on couple centred activities that can be made available in secluded

surroundings. Boutique resorts that have such facilities would most benefit from

this segment as these resorts do not need to invest in huge infrastructure that is

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mandatory for family and business oriented resort guests. Resorts that are located

in pristine and isolated locations but facing the decline stage of their lifecycle can

reposition and rejuvenate themselves toward this cluster with minimal

investment.

Implications for the second group, ‘young conservatives’ are opposite. This

cluster, the second largest, has no dependent children and is least cost conscious.

Most of the resorts entertain and cater to these traditional resort guests. This

group of people focuses on facilities and utilities. They seem to be very much

interested in rest, recreation and rejuvenation. With this cluster dominated by

tourists having traits of an individualistic Australian society (Hofstede, 1980),

they are likely to be focused on themselves and very demanding in terms of their

own needs (Pearce and Moscardo, 1984). The focus needs to be on the variety

and novelty of the activities on offer. The resorts must be well kept and

refurbished to maximise their comfort. Resorts that are located in exotic coastal

and mountain settings and well maintained will be able to attract this cluster

successfully and keep them loyal for a long time

The third cluster, ‘elite regulars’ is the largest group of people. They are older

people. The most distinguishing factor is their need for safety and security and

they stay at a resort for the longest period of time. Another requirement for this

group is the availability of a range of accommodation. They are the highest

educated with a high proportion of them being managers and professionals. Their

need to have a wide variety of activities makes them very attractive to the resort

managements. This group of people also like to enjoy a good night life that

covers going to restaurants, bars and pubs. The resorts must provide an

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opportunity for this group to enjoy and socialise with other resort users by

offering communal activities. The resort operators must look after this segment

well with a consistent service similar to ‘active conventionalists’. Established

resorts should target this group (Smith, 2004) as they have resort stay experience

and can differentiate good products and services from average ones.

The final cluster, ‘veterans’ comprises of mature couples, mature singles and

mature families. This cluster needs special marketing and management focus that

differs from the rest of the clusters. This group indicated health, safety and

security, recreational activities and cost as major factors while on a resort

holiday. The presence of security personnel or a perception of security must be

provided by the resorts. The most important suggestion is that since this group is

very cost conscious, and tends to stay for longer periods compared to other

groups, an offer of ‘all inclusive packaged’ resort stay should be considered. It is

also suggested that health and rejuvenation based activities should be offered to

this group to maintain their long term loyalty (Warnken et al., 2003).

Although a number of empirical studies have shown that the theory of planned

behaviour (TPB) is successful in explaining human behaviour in various

disciplines, the theoretical and conceptual foundations for predicting the

intention for choosing a resort accommodation by Australian tourists has not

been previously investigated. The results of this study demonstrate the partial

utility of the theory of planned behaviour as a conceptual foundation for

predicting behavioural intention for choosing a resort accommodation by

Australian tourists. Findings showed that attitude and intention had a direct

impact on the purchase behaviour, while normative beliefs were partially helpful

in predicting resort accommodation purchase behaviour. The previous experience

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of resort stay positively influenced the intention, while it did not contribute to

attitude in the same way. Perceived behaviour control or behavioural beliefs

contributed least to every dimension except attitude.

A number of salient implications can be derived from the findings of the study.

From a macro-perspective, education, occupation and maturity were three of the

most important factors influencing resort accommodation purchase. The resort

marketers must consider the professional publications for advertising in Australia

to create awareness of their property. This approach will potentially cover all

three factors that contribute to purchase behaviour. Repurchase of resort goods

and services can also be improved by contributing to the positive attitude of the

tourists by good service. The resorts must endeavour to educate and train their

personnel so that they know their jobs well as well as providing a welcoming

service to the customers. Anecdotally, resorts are reluctant to spend money on

employee training and development as there is a high staff turn over rate in the

industry as well as the transient nature of the workforce due to the seasonality

factor. Trained workers are found to be more productive and they tend to stay for

longer with the employer and require less supervision (Gee, 1993). The training

will not go waste if it creates future repurchase behaviour. The other implication

is that the resort management must reinvest in the property to keep it looking

fresh as attitudes do change when the product or service is not as expected.

Although normative beliefs appeared not to contribute as much as attitude toward

resort accommodation purchase behaviour in this study, their effect on attitude

itself was found to be a significant factor. It is crucial for the resort promoters to

facilitate the development of a positive attitude among resort tourists’ potential

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referent groups. For instance advertising proximity to shopping, availability of

beauty and spa products for the female referent groups at a particular resort will

positively influence the attitude of the principal purchaser. A place to take a

break from busy schedule and availability of activities for the children will also

positively contribute to the attitude.

The study showed an increased predictability with the introduction of past

behaviour to the mix of factors. This means that as the number of visits to resorts

increases, resort tourists’ accommodation purchase behaviour becomes stronger.

Apparently, tourists who have had good experiences are more likely to re-

patronise and help encourage potential tourists to resorts through favourable

word-of-mouth. This can be done in two ways. Firstly, the training and retraining

opportunities for the staff to enhance the quality of the service provided.

Secondly, by offering short familiarisation stays to the referent groups. As the

study suggests, a person who has had a positive experience of resort stay will

more probably revisit the resort for a longer period as well as positively

influencing the people in their social circle.

Introduction of past behaviour to the theory of planned behaviour has positive

implications as it increased the predictability of veterans to 80.8% and elite

regulars to 67.4% as well as a healthy 53.6% for young conservatives. It only

predicted active conventionalists to 28.8% which is a small consolation as they

themselves make the smallest of the resort tourists segments. Therefore, it is

expected that past experience will play an important role in predicting resort

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accommodation purchase behaviour in future marketing approaches.

Chapter 6 Limitations and Recommendations

114

Chapter Overview ______________________________________________________________

A few limitations were observed in this study. They related to

questionnaire design, data collection method or data analysis.

The limitations are discussed that generate recommendations

for future at the same time. The chapter goes on to recommend

to the marketers where to go on to from this point to get

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maximum return on their marketing budget.

6.1.0 Limitations

This study began by questioning what the role of beliefs is in forming the

accommodation purchase behaviour intentions of Australian resort tourists using

the theory of planned behaviour and the contribution of past experience on their

beliefs. This dissertation proposed and tested theoretical explanations for these

questions using questionnaires developed by Inbakaran and Jackson (2005) and

Ajzen (1991). Some limitations need to be acknowledged relating to the research

instrument, how it was administered, the role of resort managements where it

was administered and the respondents themselves.

6.1.1 Length of the questionnaire

The questionnaire was too long. An effort was made to keep the actual

questionnaire to three pages so that respondents do not reject the instrument

by just looking at it. To achieve the goal, the font size was reduced. However,

as there were 40 questions related to the TPB and past behaviour, 11 related

to demographics, 11 each to investigate reasons for resort selection as well as

activities; it still made it a lengthy one to answer. Although it is

acknowledged as a major drawback, reducing the length would have

seriously compromised the instrument.

6.1.2 Did not take into consideration the ‘off / on season’ factor

The data collection was done over a short period of time, at the time of their

stay at a resort. Thus, the results reflect only the people who were using the

resort at that time. People who travel to a resort during ‘on’ and ‘off’ periods

have a different point of view as to the reason why they travel such as price,

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activity or seeking different type of resort users. To overcome this limitation

the study should be conducted over an extended period of time to cater for

such differences.

6.1.3 Post code was not included

No provision was made to obtain the residential post code of the respondent.

This is a limitation because the marketers of resorts who wish to use the

study can not focus their attention on a specific geographical area to attract

them to their resort. The future study must overcome this so that it could be

used in a meaningful way and not remain just an academic exercise.

6.1.4 Purchase initiator / driver was not identified

The person who initiated the trip was not identified. This is a major drawback

as the beliefs of the respondent may be completely different from the person

who initiates or drives the purchase behaviour intention. It is good to know

about the person who is responding to the questionnaire but as a marketing

activity this is a futile exercise, especially if it involves a family. The results

of such a study will divert the marketing dollars in the direction of people

who do not have any say in the purchase decision, thus are wasted on the

audience. Although it is an important factor and acknowledged here,

overcoming this limitation will prove to be very difficult because of logistical

reasons unless the quantitative approach is supplemented with a qualitative

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instrument.

6.1.5 No differentiation made on the type of resort – geographically, star rating

wise or available attributes

This is one limitation that needs to be addressed in future studies. People who

go to a beach resort go there for different motivations than the ones who go

to a mountain resort. People who can afford a five star deluxe

accommodation have a different approach to the ones who use cheaper

alternatives. It is important that one type of resort should be studied in future

so that the model can be tested and refined to create specialised marketing

tools.

6.1.6 Response rate was dependent on the goodwill of the resort management

The study suffered from the universal problem of data collection. To keep the

anonymity of the resorts, the return envelope was not marked in any way. It

was observed that some resorts sent more filled questionnaires as compared

to others. This leads to the assumption that some of the resorts’ management

were more proactive than others by placing the questionnaires in the guest

rooms, collecting them and sending them back to the researcher. The only

way to overcome this limitation is to study similar types of resorts so that the

results reflect the reality.

6.1.7 Not a longitudinal study

This study was not longitudinal therefore the results should be interpreted

with caution. Ideally, the research should have been conducted over two

seasons or more to study the groups of people ‘cohort’ as that provides a

better understanding of the beliefs. This was not possible for this study as it is

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part of an academic exercise with strict timelines. It is recommended that the

future studies of resort tourists are conducted over time to confirm or reject

the results.

6.1.8 Reliability of the respondents’ motives

The use of self-reporting measures of tourist behaviour intention may be

limited in terms of reliability, a limitation to consider as self-evaluation may

have inflated some parts of the hypotheses tested. The respondents may have

had different points of view. It is possible that in this respondent population

positive attitude may have been over represented compared to the ones with

unfavourable attitude. In addition the respondents had to interpret questions

with regard to their personal experience and understanding. Another

consideration is that behaviour intentions may change after it has been

measured. To overcome this limitation the researcher or a representative

should be available to the respondents. Furthermore, a longitudinal study

would help in this regard too.

6.1.9 Cultural differences not recognised or catered for

The scope of the study was limited to anyone using a resort at the time of

data gathering and as the majority of respondents indicated English as their

language spoken at home, it assumes that most of the respondents came from

a western cultural background. The results of the study should be used with

caution if they have to be used for different ethnic groups

6.1.10 Only intrinsic factors examined to study purchase behaviour

In this study only the intrinsic factors such as demographics, beliefs and past

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behaviour were examined to investigate resort tourists’ purchase behaviour

intentions. The influence of extrinsic factors such as rating of a resort,

economic life cycle of a resort or macro-economic conditions were not

explored due to time constraints.

Conclusively, this current study provides a theoretical foundation for

understanding the past behaviour and the theory of planned behaviour on resort

tourists’ accommodation purchase behaviour intention. In addition this study is a

good first step in research about knowledge of tourism because although the

theory of planned behaviour has provided many answers in different disciplines,

the theory has been used for the first time in understanding resort tourists in

Australia. While the study has answered a few questions, it has failed to provide

clarity to some of the research questions. Given this is the first time the

constructs have been tested a further study based on the present study is

necessary to add clarity and validity to the findings.

6.2.0 Recommendations:

Despite the contributions of the present study to explore the role of beliefs and

past experience in forming the resort accommodation purchase behaviour of

Australian tourists, some results require further examination. Such a

recommendation is made because the field of study is important. The success or

failure of advertising and marketing can have a profound impact on the survival

of a resort in the medium to long term as an accounting entity and this study

120

could possibly become the foundation for such effort.

The study investigated the behaviour in the context of present behaviour as it was

conducted on the resort tourists who were using the facility at the present

moment. This present behaviour can potentially be changed by the time the same

tourists make the purchase decision in future. The marketer would be interested

in finding out about the future as that is the intent of their being. Therefore, it is

recommended that in the next study the future purchase intentions are studied so

that the results provide a basis for the marketing of resorts.

It is recommended that a segmentation study should be conducted using the latest

statistical analysis method m-plus since the current procedure takes many steps.

At one stage the researcher contemplated using SEM to investigate the beliefs

and the market segmentation but the procedure does not cater to such a

requirement. As it is, the jury is still out to confirm the benefits of using SEM

over tried and tested regression methods. It is therefore recommended that m-plus

should be explored for future use as it can segment as well as provide differing

value models for the segments along with the predictability threshold.

More studies should be conducted to segment resort tourists by socio-

demographics and then link them to attitude and intention as this study was

conducted on the basic demographic features. Such an approach will provide

more depth and clearly identify the segments to be used for marketing.

Another important recommendation is that the model developed in this study is

applied to different geographically located resorts, seasons and cultures for

validation and generalisation. This recommendation is made to counter the major

121

limitation felt in the study.

It is recommended that in future studies the model’s reliability and validity is

tested on other types of extrinsic moderating factors such as personality,

seasonality and economic forces. This approach will not only enrich the model

but also provide some keen insights into purchase behaviour intention from a

different perspective.

Future studies must be done on a longitudinal approach for the measurement of

‘behaviour’ on a sufficient smaller sample size to test the predictive power of the

model.

The effect of subjective norms (control beliefs) and perceived behavioural

control on intention was not found to be related to intention as was expected

initially. A possible explanation could be that the tourists are not concerned

about the construct when they are personally involved in the purchase behaviour

and the control belief is supplemented by other factors not investigated in this

study. Therefore, it is recommended that in future studies such modifying factors

are explored and investigated in the resort tourists’ context. Park (1991)

hypothesised that those different contexts define consumption goals, triggering

different attributes.

Finally, a concerted effort should be made to get a resort association onboard so

that the difficulties of data collection and industry cooperation could be

122

overcome.

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(http://esa.un.org/nnpp/index.asp?panel=2) (http://www.ms.unimelb.edu.au/~moshe/moshe/australia.html) (http://jobsearch.gov.au/training/default.aspx?pageld=information#EduPro) (http://www.abs.gov.au/websitedbs/D3110127.NSF/85255e31005a1918852558a c00697645/2fff0f9d836819c4ca25694c00809a6a!OpenDocument) (www.abs.gov.au/4442.0)

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Appendix A Request to participate in the study letter sent to resorts

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Ms XXXXX XXXXX Director of Sales Cable Beach Club Resort Broome Suite 411, 566 St Kilda Road Melbourne, 3004 22 March 2007 Dear XXXX Recently I was speaking to my colleague Ms XXXXX XXXX, at William Angliss Institute, about my PhD project and she suggested that I should send my questionnaire to you to get your views on the topic. I am investigating the beliefs that drive tourists to choose a particular type of resort accommodation through School of Management, RMIT University. If you agree to participate in the study, the questionnaire should not take you more than 10 minutes to complete. Once you finish, please put it in the attached envelope and mail it back to me without identifying yourself or the hotel you represent. Further, I am also looking at the possibility of putting this questionnaire in your guestrooms. There are very strict rules governing privacy and anonymity at RMIT University. Your hotel will not be identified in any manner if you choose to participate. Please call me on 9606 2400 if you are happy to further support this academic research. Thanking you Yours truly, Mukesh Sharma Senior Educator – Tourism William Angliss Institute of TAFE 555, La Trobe Street Melbourne, VIC 3000

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Appendix B Questionnaire

MANAGEMENT MANAGEMENT MANAGEMENT MANAGEMENT

Project Title: Role of Beliefs and Past Experience in Forming Resort Accommodation Purchase Behaviour: A Study of Australian Tourists Investigator:

Level 16 239 Bourke Street Melbourne 3000 Victoria Australia GPO Box 2476V Melbourne 3001 Victoria Australia Tel +61 3 9925 5919 Fax +61 3 9925 5960

Mr Mukesh Sharma, PhD Student Phone: (03) 9606 2400 Email: s3115472@student.rmit.edu.au

Project Supervisor:

Dr Robert Inbakaran, Senior Lecturer, School of Management, Business Portfolio, RMIT University Phone: (03) 9925 1534 Email: robert.inbakaran@rmit.edu.au

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Dear Sir/Madam, You are invited to participate in a research project being conducted by RMIT University. This information sheet describes the project. Please read this sheet carefully before you agree to participate. If you have any questions about the project, please ask either the investigator or the project supervisor. Who is involved in this research project? Why is it being conducted? This research is being conducted as a part of Ph D degree and has been approved by the RMIT Human Research Ethics Committee. The project investigates the resort selection processes by Australian tourists. By filling out this questionnaire, you will help us gain invaluable insights into how resorts are viewed by current patrons and how services can be improved for future patrons. What is the project about and why you have been approached? The Australian cultural expectation is that people will take time off from their busy schedules to pursue leisure activities. Most people have had some experience in staying at ‘resort accommodation’ which covers a wide range of products (ranging from a caravan park to a 5 star resort hotel). You have been randomly selected to participate in this study. If you agree to participate, what will you be required to do? If you were to participate in the study, you would be required to complete the attached questionnaire which will take you approximately 10 minutes. You should read the attached document before deciding if you would like to proceed with. What are the risks or disadvantages associated with participation? There are no risks or disadvantages associated with participation. You are not being identified in any way and your views will remain anonymous. The information you provide will only be seen by the investigator and the project supervisor. If you are unduly concerned about your responses to any of the questionnaire items or if you find participation in the project distressing, you should immediately stop participating and contact Dr Robert Inbakaran on (03) 9925 1534 at your convenience. Dr Inbakaran will discuss your concerns and suggest appropriate follow-up. What are the benefits associated with participation? There are no immediate direct benefits to the participants; however the study will help improve future products and services provided by the resort industry. What will happen to the information you provide?

The information you provide will remain anonymous throughout the study as you are not identified in any manner. The research data will be coded by the researcher and kept secure at RMIT for a period of 5 years before being destroyed. The results of this study will be disseminated in the PhD thesis and in papers for publication and conference presentation. Any information that you provide can be disclosed to a third party only if (1) it is to protect you or others from harm, (2) a court order is produced, or (3) you provide the researcher with written permission. What are your rights as a participant? You have the right to withdraw your participation at any time, without prejudice, the right to have any unprocessed data withdrawn and destroyed, provided it can be reliably identified and provided that so doing does not increase the risk to you. You also have the right to have any questions answered at any time. Who should you contact if you have any questions? Please contact the investigator, Mukesh Sharma on (03) 9606 2400. What other issues should you be aware of before deciding whether to participate? We believe that there are no ethical issues that you should be concerned about. Yours sincerely Mukesh Sharma M Business (Hospitality Management), B Sc (Biology), Diploma of Hotel Management, Catering & Nutrition, Diploma of Teaching (TAFE) Any complaints about the participation in this project may be directed to the Secretary, Portfolio Human Research Ethics Sub Committee, Business Portfolio, RMIT, GPO Box 2476V, Melbourne, 3001. The telephone number is (03) 9925 5594 or email address rdu@rmit.edu.au. Details of the complaints procedure are available from the above address or hhtp://www.rmit.edu.au/council/hrec

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p p

1. Deciding on accommodation, be it a cabin at a caravan park or a deluxe room in a five star resort hotel, is an important step when planning holidays. I am listing some of the factors that people like you may consider when choosing an accommodation. Please indicate how important each option is by placing a tick (p

p ) in the space provided.

Reason

t n a t r o p m

n i a t r e c n U

y l e m e r t x E

I

y l e m e r t x E

t a h w e m o S

t n a t r o p m i n U

a. Scenic location

b. Safety and Security

c. Recreational activities

d. Range of accommodation available

e. Distance from my city / town

f. Available information

g. Ecology and surrounds

h. Weather

i. Cost

j. Other (Please specify)

p ) for every activity.

p p

2. While staying at this holiday accommodation, how important is each of the following activities for you? Please indicate your response by placing a tick (p

Activities

t n a t r o p m

n i a t r e c n U

y l e m e r t x E

I

y l e m e r t x E

t a h w e m o S

t n a t r o p m i n U

a. Horse riding

b. Trekking

c. Water based activities (boating, swimming, canoeing, snorkelling, surfing etc) d. Sun bathing

e. Tennis

f. Golf

g. Meditation

h. Skiing

i. Bird and wild life watching

j. Night life such as night club, bar or pub etc

k. Fishing

L. Other (Please specify)

m. Other (Please specify)

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p ) in the appropriate box for every statement.

p p

3. Please indicate your views by placing a tick (p Please be aware that some statements may seem like repetition of a previous one.

Statements

e e r g a

e e r g A

e e r g a s i d

y l g n o r t S

e e r g a s i D

n i a t r e c n U

t a h w e m o S

1. It is normal for people to stay in a resort accommodation. 2. I intend to stay in a resort accommodation in the forthcoming year. 3. It will be beneficial for me to stay in a resort accommodation in the forthcoming year. 4. Most people who are important to me think that I should stay in a resort accommodation. 5. Most people who are important to me use resort accommodation 6. If I wanted to, I could stay in a resort accommodation in future. 7. It is important for me to control the choice of ‘where I stay’ 8. I have been to the same resort hotel that I visited with my parents when I was young. 9. It is fun to stay at a resort accommodation. 10. I will try to stay in a resort accommodation on my next vacation. 11. For me to stay in a resort accommodation would be a pleasant experience. 12. Some times I ask people I value, for information on resorts I should stay. 13. It is important for me to stay with my family when at a resort 14. I believe I have a complete control over where I stay. 15. I can stick to an acceptable routine when I go to a resort 16. I have taken family/friends to a resort that I have been to before. 17. It is safe to stay in a resort accommodation. 18. I plan to stay in a resort accommodation on my next holiday. 19. For me to stay in a resort accommodation is good. 20. My friends tell me that it is important to go to a resort hotel to have good vacation. 21. It is important for the young people to stay in a resort hotel where their parents have stayed in the past. 22. I always have fun at a resort because I want to. 23. I believe I can control the amount of money I spend at a resort accommodation 24. I feel comfortable revisiting resorts as they bring happy memories 25. One can meet interesting people at a resort accommodation. 26. Before my next vacation, I will investigate resort accommodation available at the intended destination whether I use it or not. 27. For me to stay at a resort accommodation would be valuable. 28. I am expected to go to and stay at a resort accommodation in future. 29. The beliefs of people I value are important to me. 30. It is mostly up to me to decide on the type of accommodation. 31. My family/friends usually go to the accommodation, I decide on. 32. I feel comfortable going to a resort as I have experienced it before and liked it.

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e e r g a

e e r g A

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b. No __

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33. It is convenient to stay in a resort accommodation. 34. I will use the Internet to search for resort accommodation. 35. For me to stay in a resort accommodation would be enjoyable 36. The people in my life whose opinion I value would approve of my resort accommodation choice. 37. The people in my life whose opinion I value stay in a resort accommodation 38. I can refuse to go to a resort if I don’t feel like it 39. I mostly select resort accommodation to suit my needs 40. I have revisited a resort after being there with family or friends Questions about you. 4. What is your gender? a. Male b. Female 5. What is your age group? a. Under 20 b. 21-30 c. 31-40 d. 41-50 e. 51-60 f. 61+ 6. What is the highest level of education you have attained? a. Primary b. Secondary c. TAFE or technical d. University 7. Please circle the category that best describes your household. a. Single b. Single with children c. Couple d. Couple with children 8. Age of the youngest child living with you. a. Less than 6 years b. Between 6 and 15 years c. Older than 15 years d. None 9. What is your occupation? 10. Are you an Australian resident? a. Yes 11. What language do you speak at home? 12. How many times have you used a holiday accommodation in the past 3 years? 13. How long have you been using such accommodation for (in years)? a. Less than 1 b. 1-5 c. 6-10 d. 11-15 e. 16-20 f. 20+ 14. On average, how long do you stay at a holiday accommodation? a. 1 day b. Less than a week c. 1-2 weeks d. 2-3 weeks e. 3+ weeks __

THANK YOU FOR YOUR TIME.

Doctoral thesis of Philosophy: Role of beliefs and past experience in forming resort accommodation purchase behaviour: a study of Australian tourists

Declaration _____________________________________________________________________

Dedication ___________________________________________________________________

Acknowledgements

Table of Contents

___________________________________________________________

Abstract ___________________________________________________________

Chapter 1 Introduction

Chapter Overview _____________________________________________________

Chapter 2 REVIEW OF LITERATURE

Chapter Overview _____________________________________________________

Intention

Author

Relationship

Year 1992 Ajzen and Driver Leisure choice

Correlation .78 .75 .73 .86 .54 .70 .80 .42* .10* .09* -.39~.40* .43* 1.20* .36 .28 .32

Chapter 3 Methodology

Name of the State

Location of Resorts-(Mountain, Lake, Coastal, Wilderness etc)

No of Resorts 68

Chapter 4 Data Analysis

Chapter Overview _____________________________________________________

4.1.0 Sample Profile

Demographic Variable

Category

Frequency Percent 198 214 139 221 52 52 111 249 152 108 152 73 47 56 236 82 123 5 140 31 31 402 10 358 54 364 47 39 77 95 64 65 72 167 231 14

Correlations

Table 4.2: Correlations

Table 4.3 (a)

Model Summary(b)

Step 1: Regression Behaviour on Intention and Perceived Behaviour Control

ANOVA(b)

a Predictors: (Constant), TotalPercCont, TotalIntention b Dependent Variable: TotalBehav

Coefficients(a)

a Predictors: (Constant), TotalPercCont, TotalIntention b Dependent Variable: TotalBehav

Figure 4.2:

Model Depicting Relationships past Intention

Step 2: TPB Second Step Model Regression

Model Summary

ANOVA(b)

.319 a Predictors: (Constant), TotalSubNorms, TotalPercCont, TotalAttitude

Coefficients(a)

6295.638 a: Predictors: (Constant), TotalSubNorms, TotalPercCont, TotalAttitude b: Dependent Variable: TotalIntention

Figure 4.3:

Correlations in the Proposed Model

Figure 4.4 (a):

Step 1: First Step of the Proposed Model

Model Summary

ANOVA(b)

a: Predictors: (Constant), TotalIntention, TotalAttitude

Coefficients(a)

a: Predictors: (Constant), TotalIntention, TotalAttitude b : Dependent Variable: TotalBehav

Figure 4.4 (b):

Step 2: Second Step of Proposed Model

Model Summary

ANOVA(b)

3.40309 .286 a : Predictors: (Constant), TotalPercCont, TotalSubNorms, TotalPastExp

Coefficients(a)

6613.835 a: Predictors: (Constant), TotalPercCont, TotalSubNorms, TotalPastExp b: Dependent Variable: TotalAttitude

Figure 4.4 (c):

Step 3: Third Step of Proposed Model

Table 4.6:

Regression Second Step of Proposed Model (b)

Model Summary

ANOVA(b)

a: Predictors: (Constant), TotalPercCont, TotalSubNorms, TotalPastExp

Coefficients(a)

6295.638 a : Predictors: (Constant), TotalPercCont, TotalSubNorms, TotalPastExp b : Dependent Variable: TotalIntention

Table 4.7:

Final Clusters by Demographics

4.3.0 Rational for above variables

Table 4.8:

Number of Cases in each Cluster

Table 4.9:

Clusters Explained

Attribute

Gender (% males) N = 411 Chi-squ = 30.08 (df = 3) p <0.0001 Age

36.5%

58.2%

43.7%