Segmenting the senior tourism market in Ireland based on travel motivations

Abstract

The mature market in Ireland as a tourism cohort is viable in terms of both size and disposable income. Not only is this, but international travel by the mature market in Ireland is a relatively new phenomenon, presenting opportunities for targeting this group. Previous research has indicated the heterogeneous nature of this cohort and the need for segmentation within this group has been documented. However, in an Irish context little or no research has been conducted into the motivations and behaviour of this tourism segment. Therefore, taking a sample of 500 mature Irish individuals, this article incorporates the multivariate techniques of both factor and cluster analyses to segment the mature tourism market in Ireland based on an examination of their push and pull (Dann, 1977) travel motivations. From this analysis, four distinctive segments are identified, namely enthusiastic travellers, cultural explorers, escapists and spiritual travellers.

Keywords

Cluster analysis factor analysis Ireland mature market motivations segmentation

Introduction

The significance of the mature market for the tourism industry has been well documented particularly in relation to the growing size and wealth internationally of this cohort. Recent research has begun to recognise the importance of the mature market and its heterogeneous nature. The population within Ireland is also ageing albeit at a slower rate than the rest of Europe. Moreover, the senior market in Ireland is now more valued than ever from a consumption perspective, as it’s the younger cohorts who are struggling with mortgage repayments and have less disposable income than previously. Therefore, a comprehensive understanding of the travel behaviour of the mature market and recognition of the diversity within this market would be valuable for industry practitioners. This article seeks to build on previous research and using both factor and cluster analyses will seek to develop a comprehensive segmentation model of the mature Irish tourism market based on Dann’s (1977) push and pull motivational framework and Pearce’s travel career pattern. More specifically, the study attempts to achieve the following research objectives:

to analyse the travel motivations and behaviour of the mature tourism market in Ireland and

to develop a comprehensive segmentation model of the over 50s Irish tourism market.

First, an overview of Ireland’s population structure will be addressed; this will be followed by an analysis of segmentation literature in relation to the mature tourism market. The next stage involves a description of the methodology and analysis, while a discussion on the findings will be provided in the last section.

Definition of the senior market

There is no definitive consensus in the literature for what constitutes a member of the senior or mature market. According to Bone (1991), anywhere between the chronological ages of 45 and 65 have been used by researchers as the beginning of maturity. The main reason for this is the fact that there is great variability in the ageing process, as individuals grow old biologically, psychologically and socially at different periods during their life. Therefore, any age limit used is unlikely to produce a meaningful definition (Moschis et al., 1997). Bartos (1980) defined the older market as those over the age of 49, while gerontologists have labelled the senior market as those over the age of 60. Other researchers have used 65 as the cut-off point, as this is the age that people tend to retire in most industrialised countries. However, the majority of retirement associations (American Association for Retired Persons, Active Retirement Association in Ireland and the UK Association for Retired and Older Persons), holiday groups (Saga Group) and hotels (over 50 hotel breaks/discounts across Ireland) consider those aged 50 and older to be part of the mature market. Furthermore, Treguer (1998) also considers the senior market as those aged 50 and over, as he believes this is an important moment in the life cycle. He postulates that at this point in life, children are becoming independent; thus, a new period of freedom is beginning for parents. Therefore, for the purposes of this study, the mature market will be defined as individuals over the age of 50, as this appears to be the age group that most marketers continue to neglect (Bartos, 1981; Konrad and DeGeorge, 1989; Sutherland, 1984).

Ageing population in Ireland

Ireland currently is in a unique position in comparison with other countries throughout the developed world as the population in Ireland is one of the youngest in Europe. Nevertheless, the population is ageing and the Central Statistics Office (CSO, 2004) estimate that by 2021, 34% of the population will be aged 50 or older. At the beginning of the last decade, the Irish government introduced policies to tackle a possible pension crisis. In recent years, measures have been taken to increase the basic state pension and ensure pensions are available to all people regardless of the employment situation (Personal Retirement Savings Account), and a National Pension Reserve Fund was created to alleviate the future costs of pensions (Considine, 2002). This implies that the future generation of retired individuals in Ireland should be financially secure.

In 2010, the average weekly household disposable income of those over the age of 50 in Ireland was estimated at €767. Furthermore, approximately one in eight older people have disposable incomes of €1000, while 4% boasted incomes of over €2000 per week (Barrett et al., 2011). Not only this but a large majority (70%) of those over the age of 50 have finished paying off their mortgage so are not inflicted with same issues of negative equity as the younger generation in Ireland. Barrett et al. (2011) also noted that the vast majority of older people in Ireland have some savings or financial assets other than property at their disposal. Researchers have also noted that travel propensity amongst the mature market is expected to increase by 5% annually (Boksberger and Laesser, 2008). Given the above factors, it can be surmised that there will be an increase in demand for tourism products by the mature market.

Segmentation

Previous research into the mature market’s travel behaviour has used numerous variables for segmenting this cohort. Age has been predominantly used (Acevedo, 2003; Anderson and Langmeyer, 1982; Backman et al., 1999; Javalgi et al., 1992; Moisey and Bichis, 1999; Romsa and Blenman, 1989) but has been identified as a crude variable for segmentation as it indicates very little about a person’s lifestyle, health, preferences, motivations and experience. Research has also been conducted into the characteristics of mature tourism participation and non-participation by identifying the unique barriers to travel amongst the mature market (Blazey, 1987; LaForge, 1984; Zimmer et al., 1995). This construct however fails to provide any evidence on the underlying behavioural factors influencing the travel decision. Tongren (1980) and Blazey (1992) focussed segmentation on the differences in travel behaviour between the retired and non-retired members of the mature market. Both researchers surmised that retirement status determines more about the individual’s behaviour rather than age, as it focuses on his or her stage in the life cycle and is therefore a more effective variable for segmentation. However, retirement status once again fails to address behavioural differences within the two segments. Cohort analysis provided the basis for segmentation in studies conducted by You and O’ Leary (2000) and Pennington-Gray and Kerstetter (2001). Both research studies concluded that significant changes in travel behaviour and needs of the mature market occur over time but travel propensity does not diminish with age.

In recent years, studies have been conducted into the tourism motivation of the over 50s and segmentation within this market is now focusing on travel motives as one of the most efficient variables for understanding consumer behaviour (Backman et al., 1999; Boksberger and Laesser, 2008; Cleaver, 2004; Cleaver et al., 1999). Early research into the senior tourism market was conducted by Guinn (1980) who described the leisure motivations of recently retired tourists and found that as this group aged, they moved from an environment where work goals took priority, to an environment which focused on leisure objectives. Guinn (1980) surmised there were five key travel motives of the senior market, namely rest and relaxation, family and friends, physical exercise, learning experience and self-fulfilment and accomplishment. Norman et al. (2001) attempted to determine whether the mature market should be primarily defined by age or other variables influence behaviour. Utilising factor analysis, they revealed six underlying push and nine pull motivations for travel within the mature market. The six push motives include escape, education, family, action, relaxation and ego, while the nine destination attributes (pull motives) are natural surroundings, good weather, tourism infrastructure, budget dining and accommodations, cultural and historical attractions, manmade attractions, people, upscale facilities and outdoor recreation opportunities.

You and O’ Leary (1999) utilised push and pull motivations in their segmentation of the over 50s UK travel market and identified three distinct segments that differed in terms of their demographics, holiday activity preference, travel philosophy and other trip characteristics. In their segmentation model, Kim et al. (2003) used demographic background, motivations and holiday concerns to segment the over 50s market in Australia. From their analysis, they conferred, there were four distinct segments within this heterogeneous cohort including active learner, relaxed family body, careful participant and elementary vacationer. In the context of the Australian tourism industry, Horneman et al. (2002) used psychographics and demographic characteristics to segment the senior Australian traveller. Six segments were identified based on their preferred holiday type, these include conservatives (45% of sample), pioneers (25%), aussies (15%), big spenders (10%), indulgers (less than 10%) and enthusiasts (less than 5% of sample). Cleaver et al. (1999) employed factor analysis¹ to identify opportunities for tourism product development based on the travel motivations of retirees in Australia. From this investigation, seven travel motive factors were identified, namely nostalgics, friendlies, learners, escapists, seekers, status-seekers and physicals.

Boksberger and Laesser (2008) segmented the senior travel market in Switzerland by travel motivation using both factor and cluster analyses. They identified three key segments within the mature market, namely time honoured bon vivants, grizzled explorers and retro travellers. They concluded that within these three clusters there may be a life cycle scenario developing with time honoured bon vivants emerging into grizzled explorers at a later stage of life, while the retro travellers tend to have both a higher educational level and professional position to the previous two groups.

This analysis emphasises the heterogeneous nature of the mature market and a crucial element in successfully targeting this lucrative segment is for marketing personnel to have a clear understanding of the needs and motivations of this diverse group. In an Irish context, there are a number of factors that distinguish the ageing population in Ireland compared with other countries internationally, the Irish population is still relatively young and the older generation in Ireland do not have a long history of international travel. These demographic and experiential characteristics infer a distinguishing feature of the Irish market, making it worthy of investigation thereby addressing a dearth in the literature.

Methodology

Data collection and sample

For the purpose of this study, a stratified random sample was devised from a commercial database of 200,000 people between the ages of 18 and 80. The main criterion necessary for the selection of this sample was that all members of the sample be over the age of 50, with a relatively even distribution of each age group. The sample was then stratified in terms of geographic region and gender. Of all, 500 respondents were selected, 164 of these were aged between 50 and 59, 164 were aged between 60 and 69 and 172 were aged between 70 and 80. The age 50 was chosen in order to determine whether age is a significant factor in tourism behaviour or whether motivations play a more dominant role. Veal (1997: 155) has identified several factors that affect the response rate in mail surveys, such as length of questionnaire, style, content, design and number of reminders. In order to overcome these weaknesses, Dillman’s (2000), tailored design method for mail surveys was adhered to, thereby resulting in a response rate of 60.4%. However, on further analysis almost 40 of these were unusable, as they were not completed properly, not completed at all or some vital components were omitted. In the end, the number of questionnaires used for analysis was 266 or a 53% response rate. This is a relatively high response rate for an unsolicited mail survey and compares well with other surveys of the mature travel market.²

Analysis of data

The design of the questionnaire incorporated open, closed, Likert scale and ranking-type questions that allowed for analysis using the Statistical Package for the Social Sciences (SPSS; SPSS, Chicago, Illinois, USA). Five-point Likert scales were employed in assessing the push and pull motivations of respondents, the activity preferences and the tourism constraints (see Tables 2 and 3 for push and pull motivations items). A large majority of the questions were analysed using univariate statistics including frequency percentages, means, standard deviations and cross tabulations. Principal component factor analysis was employed to reduce the number of variables within the questionnaire and to separately identify underlying dimensions relating to the travel behaviour of the mature Irish market. A varimax rotation, the most common choice in the orthogonal rotation method, was utilised, as it allows for easier interpretation, and the resulting factor scores were then saved for further multivariate analysis (Hair et al., 1998). The logic behind varimax rotation is ‘… that interpretation is easiest when the variable factor correlations are close to either +1 or −1, thus indicating a clear positive or negative association between the variable and the factor, or close to 0, indicating a clear lack of association’ (Hair et al., 1992: 235). This technique allows for interpretation due to its simplistic nature, while also giving a clearer separation of the factors.

Table 1.

Percentage change in agglomeration coefficient for hierarchical cluster.

Cluster	Clustering coefficient 1	Clustering coefficient 2	Differences between both coefficients	Percentage change
10	41.581	42.809	1.228	2.95%
9	42.809	43.441	0.632	1.48%
8	43.441	43.587	0.146	0.34%
7	43.587	45.952	2.365	5.43%
6	45.952	46.171	0.219	0.48%
5	46.171	47.838	1.667	3.61%
4	47.838	60.705	12.867	26.9%
3	60.705	65.248	1.543	2.54%
2	65.248	68.56	3.312	5.08%
1	68.56	–

Note: The purpose of the boldface values was to indicate that the four cluster solution in the hierarchical solution was selected as the greatest increase occurred between the third and fourth cluster.

Table 2.

Push motivation factor matrix.

Rotated component matrix*	Component
Push factors	Escaping	Exploring	Spiritual and social	Physical and excitement	Family focussed
	1	2	3	4	5
To be together as a family					0.788939
To travel within a group that share my interests			0.710593
To meet new people and make new friends			0.548515
To go places my friends have not been			0.560206
To relax	0.733864
To have fun and be entertained	0.702163
To get away from the demands and difficulties of home	0.706699
To improve health problems	0.492612
To escape from the ordinary	0.571859
To indulge in luxury	0.615669
To escape the Irish climate	0.510253
To go places I have never been before		0.804398
To learn new things about different places, people and things		0.841979
To do and see things that represent a destination's unique identity		0.770349
To experience spiritual enrichment (pilgrimages)			0.713563
To participate in a sport I enjoy at a different location (e.g. golf, fishing)				0.724953
To allow me define myself as an individual				0.583854
To find thrills and excitement				0.626706
To be adventurous (e.g. planning trip as you go along)				0.575661
Extraction method: principal component analysis. Rotation method: varimax with Kaiser normalization.
a	Rotation converged in seven iterations.

*Only variables with a factor loading greater than +0.40 were chosen in each factor.

Table 3.

Pull motivations factor matrix.

Rotated component matrix*	Component
Pull factors	Pre-arranged tour	Quality	Culture/history	Weather/food	Sports	No kids
	1	2	3	4	5	6
Destination that provides value for my holiday money		0.622186
Reputation of the destination (e.g. good quality and service, leisure centre, facilities)		0.737983
Standards of hygiene and cleanliness		0.759658
Outstanding scenery/environment			0.562098
Un-commercialised area			0.477483
Everything organised at destination (e.g. accommodation, transport, etc.)	0.815183
Guided tours with experienced tour guides	0.716505
Night-time entertainment (e.g. cards, bingo, music and dancing)	0.429734
Availability of physical activities (water sports, golf, skiing, fishing, etc.)					0.778529
Destination that does not allow children						0.677422
Safety/security of the destination		0.566343
Personal safety, safety travelling in a group	0.632225
Proximity of a medical centre	0.578979
Nice weather/climate				0.742127
Opportunity to try new foods				0.776325
Destination that caters to special needs (e.g. special diets, physical disabilities)	0.514531
Good atmosphere/ambiance		0.54536
Arts and cultural attractions			0.787269
Shopping at destination	0.468252
Opportunity to meet and socialise with local people			0.463492			-
Historical/archaeological buildings and places			0.809998
Availability of comprehensive pre-trip and in-country tourist information			0.606682
Extraction method: principal component analysis. Rotation method: varimax with Kaiser normalization.
a	Rotation converged in eight iterations.

*Variables with a factor loading greater than +0.40 were chosen for each factor.

The list of holiday activities, push motivations, pull motivations and travel constraints were all subjected to factor analysis using the SPSS. Once this was complete and the factors were extracted from the longer lists of variables, it was necessary to name each factor appropriately. A minimum level of acceptance for the significance of each variable was decided at +0.40. According to Hair et al (1992), a rule of thumb exists for determining the significance of each factor loading. In general, ‘… factor loadings greater than +0.30 are considered significant, loadings greater than +0.40 are even more important while any loading that is more than +0.50 are very significant’ (Hair et al., 1992: 239). Furthermore, when naming factors, all the significant variables for each factor are included in the interpretation, but those with the highest loading generally influence the name attributed to each factor. Following on from this, it was necessary to compute factor scores,³ as the results of this analysis were to be used as inputs in the cluster analysis.

Cluster analysis was also utilised in this study to identify the segments of the mature Irish tourism market. There are two main clustering algorithms used in cluster analysis, namely hierarchical and non-hierarchical. For this analysis, both methods were employed, in order to achieve the benefits of both and improve the overall reliability of the results (Milligan, 1980). This two-stage clustering procedure has been employed in other tourism segmentation studies of the mature market (Cleaver, 2004; Horneman et al., 2002; Norman et al., 2001; Pennington-Gray and Lane, 2001; You and O’ Leary, 1999) and is the most effective procedure for ensuring the reliability of the results. In order to determine the number of clusters to include in this analysis, two-cluster, three-cluster, four-cluster, five-cluster and six-cluster analyses were performed in the hierarchical procedure. From an evaluation of all of these solutions, it was found that the four-cluster solution was the best alternative for the purposes of this study. In addition, the clustering (agglomeration) coefficient was also used to identify the number of clusters (Hair et al., 1998). In this instance, the increase between the fourth and third cluster was the largest, thus, the four-cluster solution in the hierarchical procedure was selected. The percentage change in the clustering (agglomeration) coefficient from 10 to 2 clusters was calculated and is presented in Table 1.

The initial four-cluster solution from the hierarchical cluster analysis was then compared with cluster solutions of two, three, five and six in the non-hierarchical procedure. The four-cluster solution was found to be the most interpretable and was seen as being efficient and manageable for explaining the travel behaviour of respondents. The interpretation of clusters followed, which involves evaluating the original variables/factors that were incorporated into the cluster analysis, and determining a suitable name that describes the definitive characteristics of each cluster. As the purpose of this research was to develop a segmentation model of the mature Irish tourism market, it was decided that the names applied to each segment should reflect the dominant motivations within each group.

The final process for cluster analysis is the validation of results. Validation determines whether the cluster solution is representative of the general population (Hair et al., 1992). In order to establish this, the sample was divided into three groups with each one being cluster analysed separately, and the results of all three were then compared (McIntyre and Blashfield, 1980). From this analysis, it was recognised that although the order of each cluster had changed, the findings within each cluster for each sample were similar. For example, in terms of push motivations, any individual displaying the characteristics of the escapists also displayed the same pull motivations and holiday activities as the overall sample in this analysis. To further validate the clusters, χ² tests were performed, where appropriate, using descriptive variables that were not served as the basis for clustering.

Findings

Factor analysis

Factor analysis was applied to the push motivation variables. The latent root criterion was utilised in selecting factors, thus, only factors with an eigenvalue greater than 1 were selected. Five factors were identified accounting for 58.2% of the variance in the original 19 motivation variables. The factors were named based on the dominant theme emerging from the push motivation statements and included escaping, exploring, spiritual and social, physical and entertainment and family focussed. The resultant factor loadings for the variables in each factor are presented in the factor matrix of Table 2.

A similar procedure was completed for the pull motivation components. Six significant factors with eigenvalues greater than 1 were identified. Once again, the pattern of loadings was comprehensible, with the six factors explaining 60% of the variance and eigenvalues ranged from 2.85 to 1.03. Analogous to the push procedure, each factor was labelled in accordance with its dominant theme and includes pre-arranged tour, quality, culture/history, weather/food, sports and no kids. However, further analysis revealed that two of these factors contained only one item (sports and no kids) and thus were considered trivial and were removed from the subsequent cluster analysis. Variables with a factor loading greater than +0.40 were included and the results of the factor matrix are presented in Table 3.

Cluster analysis

Cluster analysis has several functions, but the objective for utilising cluster analysis in this study was to classify respondents into groups, who display similarities in terms of their tourism motivations. Four clusters were identified and were labelled as followers: cluster one were the enthusiastic travellers (28.6%, n = 76), cluster two were called cultural explorers (19.5%, n = 52), cluster three escapists (40.6%, n = 108), and finally cluster four were labelled spiritual travellers (11.3%, n = 30). These resultant clusters were then subjected to cross tabulations, using other variables within the questionnaire to profile each cluster according to their socio-demographic characteristics. Table 4 summarises the socio-demographic characteristics of each segment.

Table 4.

Socio-demographic characteristics of each segment.

	Enthusiastic travellers (n = 76)	Cultural explorers (n = 52)	Escapists (n = 108)	Spiritual travellers (n = 30)
Mean age	58 years	63 years	56.4 years	63.3 years
Gender
Male	47.4%	59.6%	50.5%	33.3%
Female	52.6%	40.4%	49.5%	66.7%
Retirement status	Retired 24%	Retired 51.9%	Retired 25.7%	Retired 60%
Marital status
Married	81.3%	80.4%	87.9%	79.3%
Never married	9.8%	9.8%	4.7%	6.9%
Widowed	5.3%	7.8%	5.6%	10.3%
Dependent children	None 49.3%; One 24.5%	None 78.4%; One 11.8%	None 50%; One 12.3%	None 65.5%; One 3.4%
Education
Primary	17.8%	15.7%	13.2%	24.1%
Inter cert.	23.3%	17.6%	23.6%	27.6%
Leaving cert.	23.3%	19.6%	26.4%	44.8%
Third level	35.6%	47.1%	36.8%	3.4%
After-tax income
>€20,000	17.4%	36.2%	15%	31.8%
€20,000–40,000	36.2%	31.9%	28.7%	45.4%
<€40,000	44.8%	31.9%	56.3%	22.8%

Cert.: certificate.

χ² tests were applied to further validate the results of the cluster analysis and determine whether there were any distinguishing socio-demographic characteristics between clusters. In this instance, results are considered significant at Pearson χ² test level of p < 0.05. From this analysis, it was found that age group, retirement status, number of dependent children, income level and holiday spend are significant in establishing cluster membership. On the other hand, gender, marital status, living arrangements and education do not act as discriminating variables between the clusters. Table 5 presents the results of the χ² tests.

Table 5.

χ² results of socio-demographics and clusters.

Socio-demographic variables	Pearson χ² p < 0.05
Gender	0.142
Age group	0.000
Retirement status	0.000
Marital status	0.750
Living arrangements	0.132
Number of dependent children	0.004
Education level	0.172
Income level	0.046
Holiday spend	0.022

The findings of this analysis suggest that cluster membership can change in accordance with the social characteristics of the individual. Therefore, as mature people age, for example, or move from employment to retirement, their holiday behaviour changes. Thus, certain socio-demographic characteristics have an important role to play in determining the travel behaviour of the mature market in Ireland, and how this behaviour changes over time. The travel needs of older retired seniors evolve, as they move through certain stages in their lives, for example, retirement or loss of a spouse.

The literature revealed that travel motivations are key to understanding why people travel and are useful in predicting the travel behaviour of the tourist. Table 6 summarises the univariate statistical results for each of the clusters with regard to the push and pull factors identified in the previous section.

Table 6.

Variable means and standard deviations of push and pull factors for each segment.*

	Enthusiastic travellers (n = 76), 28.6%		Cultural explorers (n = 52), 19.5%		Escapists (n = 108), 40.6%		Spiritual travellers (n = 30), 11.3%
	Mean	SD	Mean	SD	Mean	SD	Mean	SD
Push
Escaping	0.405	0.405	−1.409	0.871	0.4137	0.6493	−0.0737	0.7545
Exploring	0.5738	0.526	0.3446	1.185	−0.2235	0.916	−0.8997	0.964
Spiritual and social	0.5041	0.8973	−.3272	0.942	−0.5095	0.759	0.8711	0.681
Adventure	0.3777	1.06	−0.0252	1.062	−0.2168	0.8514	−.01323	0.979
Family	0.1651	0.9461	−0.2225	1.175	−0.104	0.8611	0.3422	1.157
Pull
Pre-arranged tour	0.5606	0.792	−0.6332	0.938	−0.3047	0.888	0.7745	0.739
Reputation and quality	0.2807	0.537	−0.8065	1.189	0.3505	0.749	−0.5751	1.288
Culture and history	0.8391	0.627	0.4522	.835	−0.6282	0.759	−0.6279	0.805
Weather and food	0.0962	0.863	−0.3519	1.237	0.1476	0.904	−0.1642	1.069

primary research – K-means cluster analysis.

*The most important holiday motivations for each group are underlined and in boldface.

Discussion

From the above analysis, four segments were identified including the enthusiastic travellers, the cultural explorers, the escapists and the spiritual travellers. The enthusiastic travellers were the first segment identified and comprise 28.6% of the sample. The mean age of this group is 57 and approximately a quarter is retired (24%). Income levels are relatively high, with almost half reporting earning annually over €40,000 after tax and nearly 50% have no dependent children in the household. An enthusiastic desire for travel is characteristic of this segment, as they consider a wide variety of travel motivations as important. A specific emphasis is placed on the exploring factor motive and socialisation, thereby the enthusiastic travellers seek to learn something new about a destination, travel to new places and meet new people. In order to satisfy these needs, the destination attributes of particular importance to this group are the environment and history of a destination and the opportunity to socialise and travel in a group. Therefore, a holiday package that is flexible and offers a variety of activities, while extolling the virtues of group travel and also emphasising the opportunity for socialisation, will be particularly appealing to this group. Crompton (1979) and Shoemaker (1989, 2000) contended that individuals seek multiple preferences from his or her travel experiences, while the revised travel career pattern of Pearce and Lee (2005) emphasises the multidimensional concept of travel motivations. The findings in relation to the enthusiastic travellers support these contentions, as they appear to have multiple motivations and interests, influencing their travel behaviour. In relation to previous research on the segmentation of the mature travel market, the enthusiastic travellers are most similar to the enthusiastic go-getters identified by You and O’ Leary (1999); the enthusiastic connecters of Cleaver (2004), Pennington-Gray and Lane (2001); active travellers and the enthusiastic female experiencers identified by Lehto et al. (2001).

The mean age of the cultural explorers is 63 and approximately 52% are retired. This segment has the highest educational attainment and the greatest proportion with no dependent children. The income of this group would be less than the enthusiastic travellers, which is a reflection of the larger proportion that is retired. Unlike the enthusiastic travellers, this group has a distinct pattern of travel behaviour. The exploring push motive factor is most influential to this group in the decision to go on holiday, while all other motives are inconsequential. Thus, the opportunity for learning on holiday and experiencing the culture of a destination motivates the cultural explorers to travel. In relation to this, the cultural explorers choose a destination based on its environment and its cultural and historical attractions. The desire for learning on holiday reflects the fact that this segment has the highest educational attainment and is the most likely to take an educational holiday. In relation to Pearce and Lee (2005) travel career pattern, this group is seeking to satisfy the self-esteem and development motivations identified.

The escapists are the third cluster identified and they represent the largest segment, corresponding to 40.6% of the older Irish population. The escapists are the youngest of all the segments (mean age 56) and they spend the most on holiday, while almost 60% have an annual after-tax income of over €40,000. Consequently, due to the actual size and spending power of this group, they represent an extremely important segment of the mature Irish travel market. Of all the segments identified, the escapists displayed a distinctive preference for international travel, over domestic travel, which emphasised their desire for travel to sunny destinations. As the name suggests, travel is viewed as a means to escape the daily routine and the mundane. The vast majority of this group are still in employment, thus the holiday is used as a vehicle, to get away from a hectic schedule. Consequently, the need for relaxation is a key motive in determining the travel decision and is reflective of the relax. The opportunity to participate in relaxing and indulging activities in a sunny destination is appealing to the escapists. In addition, the reputation and quality of the destination influences the choice of holiday, while value for money is of particular importance to this group. The characteristics of the escapists are most similar to the behaviour of the traditional mass tourist or Crompton’s (1979) need to escape from a perceived mundane environment. Therefore, the differences in lifestyle and climate between the destination and home should be emphasised in the marketing and advertising communications targeted at this group.

The final group identified are the spiritual travellers who account for 11.3% of the sample. This segment is the oldest group, predominantly female, with the largest proportion retired and the greatest proportion of widows. Unsurprisingly, members of this group have the least annual after-tax income with only approximately 23% earning more than €40,000. Once again, this segment displays a very distinctive pattern of travel behaviour, with spirituality and socialisation being the dominant themes. The travel motives of this group are religion and spirituality as well as spending time with family and friends. The traditional product offering of coach tours, to short-haul destinations, would be appealing to the spiritual travellers, as the security and safety of travelling in a group is confirmed. In terms of Pearce’s (1988, 1991) travel career ladder, the spiritual travellers are seeking to fulfil the relationship motivation by maintaining and initiating relationships and travelling within a group/family. Furthermore, this group are the most dependent of all the segments, thus, corresponding to the traditional image of senior travellers. The older age of this group, the higher proportion in retirement and the higher proportion of widows, possibly stimulate the desire for spirituality and socialisation on holiday. Similarities with previously identified segments of the mature market are evident (You and O’ Leary’s, 1999, passive visitors; Shoemaker’s, 2000, active retirees; and Kim et al.’s, 2003, careful participant). However, the significance placed on religion by the spiritual travellers appears to be a motivational factor specific to the Irish market or the percentage of such travellers in other countries may not have been substantial enough to warrant inclusion in the other studies. The value placed in religion by the older generation in Ireland, as well as the significance of the Catholic Church in Irish society, contributes to the importance placed in religion on holiday by the mature market. This suggests that the spiritual travellers are a unique segment of the mature Irish travel market, thus, further emphasising the significance of this current study and its overall contribution to the body of knowledge and tourism research specifically. As this group is the oldest, they will be more concerned with the increasing costs of travel insurance with age and the single supplement charge often applied in hotels than the other groups. In targeting this segment, the benefits of the holiday to the individual should be emphasised. In this instance, spirituality, socialisation and the safety associated with group travel should be effective in motivating this group to travel. Finally, of all the segments identified the spiritual travellers appear to be unique to the Irish market. Although similarities with segments identified in other senior tourism studies are evident, the dominant theme of religion in the travel decision process of the spiritual travellers is distinctive from the findings of these other studies.

Conclusion

This article has furthered the understanding of mature tourism segmentation and the travel behaviour of the mature market in Ireland. In addition, the methodology developed in this thesis could similarly be used in consumer behaviour studies of the mature market in other industries. The service sector and the leisure industry in general could benefit from a segmentation approach in developing products and advertising aimed at the senior market. Further research into the complex and varied tourism behaviour of the senior market is also required particularly in the context of a changing economic environment within Ireland. Furthermore, a similar methodology could be used for the mature inbound market to Ireland in order to provide practical solutions for both public and private tourism operators in successfully targeting this increasingly important tourism market.

Footnotes

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Notes

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