Understanding Senior Self-Drive Tourism in Australia Using a Contingency Behavior Model

Abstract

This aticle is the first application of the contingency behavior model to understand the behavior of grey nomads to changes in the availability of accommodation facilities when visiting regional Queensland of Australia. Using a pilot survey of 90 respondents, it was found that grey nomad future visit trip would be adversely affected by a decrease in accommodation facilities and an increase in travel costs. However, for an increase in accommodation facilities, repeat visitation takes place but with a smaller impact (than the decrease) and, interestingly, travel costs become insignificant. In addition, income levels proxied by education, and social events are significant determinants of future visits. These findings provide important policy considerations for effective management and understanding of the self-drive silver market for tourism.

Keywords

grey nomads contingent behavior repeat visitation accommodation

Introduction

The senior travel market in Australia as well as in other developed countries is growing significantly (Reece 2004; Hsu and Lee 2002). Currently, Australia’s 2.97 million seniors spend AU$895 million on domestic travel annually, with an expected increase to AU$2.3 billion by 2051 (Queensland Government 2011). The literature on senior travelers profiles them to be about 55 years and older, retired or semiretired, and who belong to a segmented market. For instance, those of limited mobility and/or liking for the sea are more inclined to go for sea cruises; some are outbound tourists similar to any other typical overseas-going travelers, and there are others who go on organized domestic tours or coaches within their own country, yet another group known as the grey nomads self-drive to explore local destinations.

This study focuses on the last group of senior travelers as interest on grey nomads is relatively recent and studies on them are limited. These include Viallon (2012), Patterson, Pegg, and Lister (2011), Glover and Prideaux (2009), Patterson and Pegg (2009), Cridland (2008), Prideaux and McClymont (2006), Onyx and Leonard (2005, 2007), Horneman et al. (2002), Mings (1997), Mings and McHugh (1995), and McHugh and Mings (1991, 1992). This study adds depth to the existing literature by providing an empirical analysis using the contingency behavior model to understand how grey nomads’ demand may be sensitive to the lack and availability of places to accommodate/park their vehicle for an overnight stay.

A survey undertaken in regional Queensland in Australia is used as a case study for this empirical exercise. The phenomenon of grey nomads is set to increase in Australia given the ageing of the baby boomer generation. This is also the case for other developed countries as it has been forecasted that the number of people over 60 will more than double to about 22% of the world’s population by 2050 (Magnus 2009). More specifically, caravan parks and showgrounds increasingly constitute an important basic need for a significant number of grey nomads in Australia. For instance, a study of the caravan industry in Western Australia found that grey nomads represented 40% of caravanners who stayed at caravan parks for accommodation on a road trip (EDC 2011) and 75.6% of the grey nomads in Queensland traveled in caravans (DEEDI 2010).¹ To date, there has been no comprehensive study on the accommodation needs of the grey nomads.

Second, this study is important given that the grey nomad market is significant in more ways than one in Australia. According to the CMCA (2009), grey nomads spend on average AU$500 per vehicle (containing a couple) a week while on the road. With an estimated 80,000 recreational vehicles on the road at any given time, the grey nomad tourism spend is very substantial (CMCA 2010). Grey nomad tourism is also closely linked to rural and regional tourism (DEEDI 2010; Greiner et al. 2004) and is regarded as a regional saviour. The elderly tourists are often keen to explore their “own backyard” to experience the outback lifestyle (DEEDI 2010; Baillie 2008).² This form of tourism creates employment opportunities for these rural regions whose dependency on agricultural activities has seen a decline due to migration of the younger generation towards cities for better jobs as well as strong competition from developing countries’ agricultural products.³

Grey nomads have been identified as a significant segment of the domestic drive tourism, which is the “the bread and butter” for Queensland tourism industry (Queensland Government 2009). Thus the Queensland government is keen to manage and develop the tourism market for the grey nomads who made up 28% of all domestic visitors to Queensland in 2009 (DEEDI 2010). In 2010, an official inquiry revealed that an inhibitor to tourism growth was a lack of (or inappropriate) accommodation for this group of tourists. Queensland has 600 caravan park sites but this is inadequate in the face of rising demand for caravan and camping accommodation coupled with the decline in the overall number of commercial and caravan parks (ibid). In line with this concern, the Queensland Government (2009) recently endorsed a caravan park policy to accommodate the needs of the grey nomads. This has taken the form of clearly setting up the process for creating a reserve for recreation using state land to enable grey nomads to camp overnight.

Thus, this study responds to an identified need for further research in the area of accommodation for grey nomads in Australia. This is done using a pilot survey of grey nomads who were attending the annual State Rally of the Queensland Caravan Club held in Beaudesert which is in regional Queensland. The data are then used to estimate a contingency behavior (CB) model, a methodology that is increasingly applied in other areas of tourism (see Morton, Adamowicz, and Boxall 1995; Eiswerth et al. 2000; Hanley, Bell, and Alvarez-Farizo 2003; Kragt, Roebeling, and Ruijs 2009; Rolf and Dyack 2011). The advantage of the CB model is that it is able to consider the impact of a hypothetical scenario of change in amenities, site, or environmental quality. More specifically, the objective of this study is to examine for the first time the responsiveness of grey nomads’ trip frequency to an increase and a decrease in the number of accommodation facilities such as caravan parks and showgrounds. This is important for understanding the future of grey nomad visits and the factors that affect their trip frequency.

The rest of the article is organized as follows. The next section describes the survey undertaken to study the above-mentioned aspects of grey nomad consumer behavior. This is followed by the methodology used and empirical results. The last section concludes.

The Survey

The grey nomads in this survey belong to one of the 26 affiliated clubs of the Queensland Caravan Club and attend the annual State Rally held in various parts of regional Queensland. This survey was undertaken at the week-long 2012 State Rally held in Beaudesert of the Maryborough region in Queensland. Information from the grey nomads was elicited using a paper-based survey provided to them in their Rally bag when they registered at the site and it was administered in a drop-off format after they had completed it. The response rate was 50% and we obtained 90 completed usable surveys for analysis. The survey collected data on a range of recreational and attitudinal issues of the grey nomads, their sociodemographic characteristics, the number of trips made to the Beaudesert region in the past five years,⁴ future visits based on changes in accommodation facilities, as well as travel cost (TC) incurred in their trip.

The TC was assessed for a round trip as 96% of the respondents indicated that attending the Beaudesert Rally was the main reason for the trip. Hence, multipurpose or multidestination journeys were not a problem of concern. The travel expenditure consisted of fuel cost, expenditure on food consumed along the way to the site, and accommodation costs on the journey to the location as well as at the Rally site. Often opportunity costs in terms of travel time are also included in TC models but in our context, 92% of the travelers are retired; the opportunity costs are insignificant and were thus not included. The mean TC was found to be AU$63.07 per caravan per day with a standard deviation of AU$33.13 as reported in Table 1.

Table 1.

Survey Statistics on Grey Nomads Visiting Beaudesert.

	Mean (Standard Deviation)
Average age of respondents (years)	67.23 (6.47)
Average length of stay in Beaudesert (days)	7.23 (1.83)
Average length of total trip (days)	10.52 (7.35)
Number of visits in the past 5 years	6.02 (7.36)
Number of likely visits in the next 5 years	3.34 (4.93)
Travel cost per day (AU$)	63.07 (33.28)
Believe that caravan accommodation is insufficient in the region^a	3.58 (0.94)
Importance of organized social activities^a	4.11 (1.09)
Number of retired years	7.50 (5.81)
	Percentage of Respondents
Activity attended
Vineyard tour	10.23
o’Reilly National Park	9.09
Outback spectacular	36.36
Retired	92.48
Type of retirement fund
Self-funded	37.35
Full pension	26.51
Part pension	36.14
Education level
Did not finish school	19.82
High school certificate	37.50
Technical college/institute	26.14
University	16.54

Based on a Likert-type scale of 1 (strongly disagree) to 5 (strongly agree).

Summary statistics based on the survey data are also provided in Table 1. The average number of trips made in the last five years was 6.02. This means that at least one trip per year to the Beaudesert region was made by every caravan owner. The average age of respondents was about 67 years, with about 92% of them being retired. About 63% of them are on a full or part pension and 43% of them have more than a high school certificate. On average, there is a perception among the respondents that there is a lack of caravan parks in the Beaudesert region and this reinforces the importance of the issue examined in this study.

The average length of stay in Beaudesert is reflective of the week-long State Rally attended by the grey nomads, and overall, their total trip spans about 11 days. Some of the activities that the grey nomads were involved in include a vineyard tour, visiting a national park, and attending the popular outback spectacular, which is a musical showcase of the rural Australian lifestyle. The grey nomads also ranked social activities as an important motivation for their trip.

The Contingency Behavior Model

The CB model offers the scope to study consumer behavior and the responsiveness of consumers to changes beyond the range of existing data. In this survey, respondents were first asked to indicate their future visit rate in the next five years if there was no change in the number of caravan parks and showgrounds. Then they were asked to respond to potential (hypothetical) changes in these accommodation options, which comprised both positive (increases in accommodation venues) and negative changes (decrease in accommodation venues). These were captured in the CB scenarios below.

How many times are you likely to come back for a visit to the Beaudesert region in the next 5 years if there were more budget type caravan parks and showgrounds. There are currently about 10 such types in the area.

Change in the number of caravan parks and/or showground	Number of likely visits in the next 5 years
1. No change
2. If 5 more
3. If 10 more
4. If 5 less
5. If 10 less

The importance of the above issue was first determined after a focus group discussion was undertaken with the Queensland Caravan Club Committee to consider if it was a realistic scenario for analysis. Then a pre-test on the boundaries for the positive and negative changes in accommodation availability was carried out before deciding on the exact CB scenarios. Our variable of interest is a count of expected future trips to the Beaudesert region. This is a nonnegative integer number and belongs to the group of count data models whose specifications take the form of Poisson, negative binomial, or truncated negative probability models (Cameron and Trivedi 1986; Hellerstein 1991; Hellerstein and Mendelsohn 1993). Another advantage of count data models is that they can be fitted to nonnormal data, such as those characterized by large numbers of zero or single visits (Rolf and Dyack 2011). Drawing from Haab and McConnell (2002) and Hanley, Bell, and Alvarez-Farizo (2003), the probability that a recreational user i will make n trips to a specific site is modeled as an exponential function where the coefficient λ represents both the mean and variance of the trip distribution as shown below:

Prob (y_{i} = n) = exp (- λ_{i}) * λ_{i}^{n} / n!

where the Poisson parameter λ is conditioned on some observed explanatory variables, X, such that ln λ_i = β′ X_i . The disadvantage of the Poisson model, however, is the restrictive assumption of equality between the mean and variance of the trip distribution. To relax this assumption, we adopt the negative binomial model that nests the Poisson model as a special case by extending the Poisson model with the introduction of individual effect, u_i. The model may then be written as

\ln λ_{i} + ln u_{i} = β^{'} X_{i} + ε_{i} .

The probability of observing y_i in equation (1) conditional on both X and u, has the same structure as the Poisson distribution. In order to evaluate this distribution conditional only on X, a distribution for u_i = exp(ϵ_i) must be specified. If a gamma density is assumed, the conditional distribution, f (y_i|x_i), is a negative binomial with mean λ_i and variance λ_i (= 1 + ϕλ_i,), where ϕ is the parameter of the gamma distribution. The null hypothesis ϕ = 0 can be used to test the appropriateness of the Poisson model. Alternatively, it can be seen from Table 1, with the standard deviation of future trips being 4.93, that the variance which is 24.30 (that is, 4.93²) is over twice the sample mean, an indication that the counts/visits are likely to be overdispersed (Cameron and Trivedi 2005). Thus the negative binomial model is preferred over the Poisson model as it corrects for the overdispersion found in the data.

The negative binomial demand function to be estimated is given by modeling the future trip rate at current conditions, δ, as the dependent variable on the left-hand side of the equation below

δ = β_{0} + β_{1} T C + β_{CB} CB \dots + β_{n} X_{n}

and this is specified as a function of the constant given by β_o, the TC, the CB variable capturing the various scenarios discussed earlier, and finally, a host of explanatory variables, X, which consist of activities undertaken, respondent characteristics, and their various interests. Three variations of the model were estimated. In one, all changes (positive and negative changes in accommodation facilities) were combined, while the other two captured the effect of positive and negative changes separately on the future trip frequency. The separation of the positive and negative changes provides some interesting results for policy as discussed below.

The combining of real and hypothetical behavior⁵ permits the estimation of the model specified in (3) in a panel data format. The hypothetical responses for each of the scenarios were “stacked” to create the panel data. Thus there are five observations per respondent. The panel analysis thus extends the initial sample size of 90 to 440 in the first model and 176 in the second and third models (see Table 2). The gains in efficiency from panel estimation reduce the sample size necessary to achieve more accurate estimates (Englin and Cameron 1996).

Table 2.

Estimates from Contingency Behavior Model.

	All Changes in Accommodation Availability	Positive Changes in Accommodation Availability	Negative Changes in Accommodation Availability
Constant	12.6587*** (3.306)	14.1564*** (3.7451)	14.7012*** (4.1044)
Length of total trip	−0.0109 (0.0172)	−0.0053 (0.0161)	−0.0261 (0.0231)
Travel cost per trip per day	−0.0003** (0.0002)	−0.0013 (0.0048)	−0.0017**(0.0008)
Activity
Vineyard tour	−0.6103 (0.4503)	0.7390* (0.4381)	−0.6877 (0.6426)
O’Reilly national park	0.1241 (0.4560)	0.0959 (0.4268)	0.1528 (0.6437)
Outback spectacular	−0.5229** (0.2356)	−0.5372** (0.2207)	−0.7805** (0.3484)
Importance of organized social activities	0.3106*** (0.1051)	0.2972*** (0.1007)	0.3913** (0.1802)
Belief that accommodation facilities in the region are insufficient	0.0574 (0.0936)	0.0903 (0.0857)	0.0184 (0.1351)
Future change in number of caravan parks and showgrounds (CB variable)	0.0476*** (0.0084)	0.0469* (0.0296)	0.1192** (0.0507)
Education level	0.2435** (0.1131)	0.1945* (0.1055)	0.3841** (0.1723)
Number of retired years	0.0103 (0.0213)	0.0123 (0.0203)	0.0401 (0.0346)
Full pension	−0.0241 (0.1294)	−0.1208 (0.1871)	−0.4275 (0.4670)
Part pension	−0.1504 (0.1334)	−0.3411 (0.2338)	−0.7950* (0.4249)
Number of observations	440	176	176
Log likelihood	−793.07	−406.52	−242.97

Note: Standard errors are in parentheses.

***

p < .01, **p < .05, *p < .1.

With the panel model, estimation can be done using the fixed or random effects approach. Here, the random effects approach is chosen as individual characteristics may not change sufficiently between observations given that there are five scenarios modeled for the same person (Englin and Cameron 1996). Furthermore, drawing on the explanation of Rausch, Boxall, and Verbyla (2010), the random effects estimation is appropriate if the focus is on variations in accommodation availability across within-individual observations and the changing accommodation availability measures are uncorrelated with the other explanatory variables in the model.

Results and Analysis

The results of the estimated CB model are provided in Table 2. The appropriateness of splitting the sample into positive and negative changes was tested using a Likelihood ratio test. The log-likelihood statistic for the model involving both positive and negative changes is 793.07. The test statistic, which follows a chi-square distribution, can be calculated as −2 × [–793.07 – (–406.52 – 242.97)]. This equals 287.16 and is more than the chi-square statistic of 26.12 with 14 degrees of freedom at 5% level of significance. This indicates a significant difference between the models.

The interpretation of the results is the impact on the expected frequency of future trips. It can be seen that the CB variable given by changes in access to accommodation in the Beaudesert region is significant in affecting future visit rates in all three models. The impact is however greater (0.1192 compared to 0.0469) when there is a negative change in accommodation facilities than a positive change. This nonlinearity in consumer behavior is in line with the findings of asymmetric consumer behvior by Nicolau (2012), Kobberling, Schwieren, and Wakker (2007), Klapper, Ebling, and Temme 2005, and Krishnamurthi, Mazumdar, and Raj (1992), which draw upon the Prospect Theory of Kahneman and Tversky (1979).⁶ These studies found that consumers reacted more or are more sensitive to a negative situation such as a price increase. This finding on accommodation availability adds depth to the existing literature which is mainly focused on price. Thus, consumer decisions do not depend on the absolute level of available accommodation but rather on their deviation (changes) from some reference level of available accommodation identified in the CB scenario.

Yet another nonlinearity in consumer behavior is found in the impact of TC. The estimated coefficient for TC was negative and significant for the two models apart from the positive change model. This suggests that future trip frequency will rise with an increase in accommodation facilities but TC will have no impact on trip frequency. Thus, even in the face of increasing costs, grey nomads will still travel if more accommodation is provided. However, for the negative change model, grey nomads would consider increases in TC as an additional deterrent (given the significance of this variable) in the decision to visit the area again. This is in line with the findings on asymmetric consumer behavior that increased risk and uncertainty avoidance, in this case of not finding accommodation, leads to a more cautious approach to consumer decision making.

The inclusion of other variables in the model shows that future trip frequency is associated with some heterogeneity. For instance, compared to the self-funded (this is the benchmark for comparison), the part pensioners behave quite differently in their future trip frequency. They are less keen to make repeat trips, particularly with a decline in accommodation facilities. It could be that this group of people are more strapped for cash than those who are self-funded or on a full pension. The education variable on the other hand is positive and significant. If this is any indication of the income levels of the grey nomads, then those who are better able to afford will come back to the Beaudesert region while those on a tight budget may choose to explore other areas in Queensland.

One striking feature that encourages future trip frequency in all three models is the organized social activities. This highlights the grey nomads’ need for interaction with others in a formal/organized setting and the importance of getting to know others from other caravan clubs. The need to get away and connect with like-minded individuals rather than solely spend time to sight see could well be a motivating factor to revisit the region. And often in their interactions, the grey nomads find out about areas they have not known before, for their next visit. These established channels of communication via word of mouth is still very much part of the information network of the grey nomads as to where to travel. The social network and its positive impact on the well-being of the grey nomads keep them healthy and this has been purported to be a good model for aged tourists (Sedgley, Pritchard, and Morgan 2011; Higgs and Quirk 2007). The social events also point to the associated benefit the grey nomads derive from caravan club affiliation. Future research can seek to compare this finding with those grey nomads who do not belong to a club.

In terms of activities undertaken, those who went on the vineyard tour are likely to come back again only if there are more accommodation facilities. As the Queensland region has several other wineries that offer wine tasting opportunities and a chance to purchase some new or favorite wines, the grey nomads can choose not to come to Beaudesert unless they can be assured of sufficient accommodation. The visit to the national park has no bearing as it is likely that the grey nomads have seen and explored the park sufficiently that it has become less of a draw for future visits. The outback spectacular event on the other hand has a significant negative impact on future visits. This is a dinner and entertainment event that primarily showcases the rural outback lifestyle of Australia. Being a one-off event with little variety, it is highly unlikely to be an attraction for repeat visitors.

Table 2 shows that trip length, number of retired years, and the perception of insufficient accommodation facilities have no impact on the future visit rate of grey nomads but they provide interesting insights. For instance, the length of trip and retirement years highlight the fact that grey nomads are not constrained by time when they decide to revisit. Having time on their hands means that grey nomads can choose to revisit whenever they want to as they are more flexible as to where they can stay. In fact, some grey nomads stay in free camping areas in the bush and/or rest stops along the way, in addition to caravan parks and showgrounds. The grey nomads can also book their accommodation beforehand or during off-peak seasons. Hence future visits are not affected by their perception/knowledge of insufficient accommodation as they can find alternatives. This is verified by the evidence that about 43.33% of the surveyed respondents have visited the Beaudesert region at least three times before in the last five years alone. Repeat visitation is an important feature of grey nomad tourism that needs to be tapped into. Further evidence is found by Greiner et al. (2004) who report that grey nomads have visited the Carpentaria Shire in northwest Queensland four times.

Lastly, it must be acknowledged that the above discussion based on the empirical models is not definitive by any means. In fact, there are several limitations of this study that need to be borne in mind. First, as a pilot survey, this study only surveyed those who belong to the Queensland Caravan Club. Thus in the next comprehensive survey, a broader sample of grey nomads (that is, nonclub members) will be included and this can be used to analyse if the grey nomad market is segmented in its behavioral response. Second, one limitation of the CB model is that the inclusion of different substitute sites (not done here) to the Beaudesert region may lead to different results. It has been argued by Kragt, Roebeling, and Ruijs (2009) that substitute sites may have an impact on the behavioral response for the various scenarios. Third, it has been noted that careful validity tests for CB remain rare (Eiswerth et al. 2000; Loomis 1993). Grijalva et al. (2002) explains that unlike the contingent valuation model, there are few validity constructs on the CB model as the latter is restricted to the construction of use levels only. Nevertheless, a general concern about CB models is whether intended trips are a robust indicator of actual trips (Hanley, Bell, and Alvarez-Farizo 2003). While the concern on accommodation examined in this study is a real issue as identified by the Queensland government, when and how it will be addressed have yet to be seen. Thus, it is not possible to do a survey to check on the validity of intended visits collected in the survey used in the study. There however exist some supporting evidence from Loomis (1993), Grijalva et al. (2002), and Haener, Boxall, and Adamowicz (2001) that CB is an appropriate indicator of actual choices.

Conclusion

The study involves the first application of the contingent behavior method to examine grey nomad behavior. Although this case study is based on a small pilot survey, the panel data format enlarges the sample size, making the analysis relevant for policy. This is important because of the significance of the silver market in tourism. According to Roy Morgan Research (2007), the greatest opportunities for growth in the Australian tourism industry for the next 10 years is the grey nomad market. The elderly people of the future will also be healthier, wealthier, and better educated, with many funding their own retirement. Baillie (2008) notes that senior travel trends in caravan and camping accommodation is expected to increase from 15.7 million nights in 2004 to 20 million nights in 2011.

A number of key conclusions can be suggested for policy consideration keeping in mind the limitations of the study and model. First, the future visit trip of grey nomads would be adversely affected by a decrease in accommodation facilities and an increase in travel costs. However, with an increase in accommodation facilities, repeat visitation will take place but with a smaller magnitude compared to a decrease in accommodation. Thus, grey nomads exhibit asymmetric response to changes in the availability of accommodation. They also behave differently when it comes to travel cost consideration. For instance, in the positive accommodation change model, travel costs become insignificant. This finding implies that access to accommodation facilities is far more important than travel cost considerations for future visits of grey nomads. Thus, to encourage more trips, tourism authorities should develop more accommodation facilities, and charge higher prices to recover the costs of developing these facilities.

Another implication of the above finding is that in the face of rising fuel costs, which may affect other tourist groups, or global financial events that may affect inbound international tourism, the grey nomad market may well be a resilient and sustainable form of tourism as travel costs are not a concern as long as there is an increase in accommodation. The flow-on effects are especially important for the regional and rural communities of Australia. The overall expenditure of grey nomads can also be considerable as Robson (2007) notes that the average length of stay of the grey nomads is about four times the other tourist segments.

The study also found that some level of variety in the area’s attractions is important for future visits of grey nomads in addition to income (proxied by education) of grey nomads. Organized social events featured significantly throughout all the models, highlighting not just the importance of social networks for grey nomads but also showing the importance of the role of a caravan club. This finding needs more attention in future research with a comparison of grey nomads not affiliated to a club. In conclusion, although grey nomad travel is a growing social phenomenon, it remains an area where quantitative research using empirical models is very limited with the exception of Cridland (2008).⁷ This study highlights the need for this gap in the literature to be addressed.

Footnotes

Acknowledgements

I would like to thank Sharon Chang for her excellent research assistance. I am also grateful to the comments of the three referees, which have improved the quality of the article.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by a Faculty of Business, Economics and Law Research Grant from University of Queensland.

Notes

Author Biography

Renuka Mahadevan is an Associate Professor in the School of Economics at the University of Queensland.

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