Determinants of length of stay: A practical use of survival analysis

ARTICLE IN PRESS

Tourism Management 28 (2007) 736–746 www.elsevier.com/locate/tourman

Research article

Determinants of length of stay: A practical use of survival analysis Ummuhan Gokovalia, Ozan Bahara, Metin Kozakb, a

Department of Economics, Faculty of Economics and Administrative Sciences, Mugla University, 48170 Ko¨tekli, Mugla, Turkey b School of Tourism and Hotel Management, Mugla University, 48170, Turkey Received 6 December 2005; accepted 9 May 2006

Abstract The principal purpose of this study was to analyze the determinants of tourists’ length of stay at a destination. Data were collected through a questionnaire survey conducted in the summer of 2005. Exceptionally different from other similar studies, this study employed survival analysis to analyze the data. The findings indicate that, out of 39 variables, 16 significantly associated with tourists’ decisions about the length of their stays during a summer vacation. More specifically, nationality, education, income, experience, familiarity and daily spending are among those as the major determinants of the length to stay. An increase or decrease in such variables is accompanied by a significant increase or decrease in the length of stay. Implications for both the theory and the practice are discussed. r 2006 Elsevier Ltd. All rights reserved. Keywords: Survival analysis; Length of stay; Decision making; Market segmentation; Economic factors

1. Introduction The literature considers the length of stay as one of the major issues that need to be resolved in a visitor’s decisionmaking process (Decrop & Snelders, 2004). In vacation decision making, visitors weigh up the benefits of different vacation alternatives, assess the cost of each alternative and the length of stay they can afford to reserve and pay for, by taking into consideration their financial and time constraints (Alegre & Pou, 2006). For a visitor, the decision where to go, how and what to do involves the evaluation of a series of choices, based on economic and demographic factors, including the budget allocated for vacations, the time available and with whom to go (Fesenmaier & Jeng, 2000). For example, the income elasticity of one segment was found to be higher than that of the other segment. This means that the former segment is more concerned about changes in their income levels while deciding to go on a vacation (Witt, 1980). Using these arguments as a departure point, one may also suggest that it is important Corresponding author. Tel.: +90 252 211 18 56; fax: +90 252 223 91 64. E-mail addresses: [email protected] (U. Gokovali), [email protected] (O. Bahar), [email protected] (M. Kozak).

0261-5177/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.tourman.2006.05.004

to bear in mind the existence of several determinants affecting consumer behavior in pure marketing (Oliver, 1999) as well as in tourism marketing research (Moutinho, 1987), as indicated below. Factors disrupting flow to a destination may be economic or temporal features appearing in touristgenerating countries as well as in tourist-attracting countries (tourist destinations), and include age, income, occupation, personality, cost, time, motivation, distance, risk and availability of alternative destinations. Consumer perceptions may influence the choice of a destination, the consumption of goods and services while on vacation and the decision to stay for a certain period of time (Stevens, 1992). As visitors are offered a variety of destinations, more choice of accommodation, and a wide range of activities and tours which are designed for specific interests for each year, it has become fairly difficult for an individual to decide where to go and how long to stay (Laws, 1995). At this stage, Morrison (1989) presents two criteria on the supply side, namely, objective and subjective, which help visitors decide which one meets their own criterion best. While the objective criterion includes prices, locations and physical characteristics of facilities and services, the image of a place is considered as the subjective criterion. On the demand side, a visitor’s sociodemographic profile (age,

ARTICLE IN PRESS U. Gokovali et al. / Tourism Management 28 (2007) 736–746

income and family status) and psychographic attributes (e.g., motivations or preferences) are important personal factors in choosing a location for a vacation and the length of stay (Alegre & Pou, 2006). This paper investigates determinants of the length of tourists’ stay on their vacations in Bodrum, Turkey. The rationale for the investigation of the length of stay could be justified in several aspects. First, it is important to estimate the major determinants of the length of stay. Once the factors that affect the length of stay are determined, policy drawing would be possible to strengthen and/or reduce the length of stay so that economic benefits would be maximized. In such an analysis, one should take into account the assessment of several factors that might have a potential influence on the duration of tourists’ stay. The length of stay may change according to sociodemographic profiles of tourists on the demand side as well as based on their holiday characteristics on the supply side. In this study, a questionnaire form was designed and distributed among tourists visiting Bodrum, located on the southwest coast of Turkey, in the summer of 2005. This paper utilizes a wholly different approach to identify the main determinants of duration of tourists’ stays at micro level, which specifically refers to survival (or duration) analysis. In survival analysis, the main focus is not only the duration of events (length of stays in our case) but also the likelihood that the event will end in the next period given that it lasted at least till the period t (Greene, 2000). The question to be answered here is the probability that tourists stay, for example, 10 days at a destination, given that they stayed nine days. A useful and convenient method to utilize this aspect of the data is the hazard rate specification. The duration of stay is formalized in terms of the hazard rate, which gives the probability of termination of staying at time t knowing that it was in force until t. 2. Literature review The length of stay and its effective analysis could be an indicator of the profile of tourists visiting one destination and their propensity to spend while on vacation. Recent research findings show that overseas travelers visiting the US and, wishing, for example, to visit cultural and natural attractions (e.g., museums and national parks) are likely to spend more time and money than those engaged in other forms of tourism (Judith, 1999). Investigating the amount of time tourists devoted to visit a destination aids tourism authorities to decide on the type of tourism product(s) they will offer and the type of tourism demand they intend to attract. In this context, there have been a number of empirical studies that consider the length of stay as a part of market segmentation variable in estimating the determinants of tourist spending (Davies & Mangan, 1992; Legoherel, 1998; Mak, Moncur, & Yanomine, 1977). According to their findings, the length of stay has a critical role in total tourist spending on a vacation, despite the fact that the findings are contradictory. Findings of some

737

studies support the proposition that those staying longer spend more than those with shorter visits (Mules, 1998; Spotts & Mahoney, 1991). In contrast, those with shorter stays are likely to spend more than longer-staying visitors (Mok & Iverson, 2000). Considering the length of stay as a substantial part of quantitative measures in estimating tourism performance could provide destinations with some advantages, such as giving visitors an opportunity to have more experiences at the destination and positively influence the amount of money they spend on vacation (Kozak, 2004). The amount of money spent increases as the opportunities to have more experiences increase. As the longer the tourists choose to stay, the more likely they are to become aware of facilities and services at the location where they are staying and also at neighborhood locations. This will widen the size of the multiplier effect of tourism revenues at the destination. The length of vacations may also reflect the attractiveness of a destination; however, several other factors may also influence the length of vacation, such as the availability of free time that can be devoted, the availability of flexible package tour deals, the level of prices, the number of people in the party, familiarity, etc. Much is known about the type and power of factors that influence the choice of a destination (Moutinho, 1987; Um & Crompton, 1990; Woodside & Lysonski, 1989). Despite the fact that the length of stay takes among the first stage of vacation decision making (Decrop & Snelders, 2004), it has received little attention in the academic world. Among these studies are those published very recently (Alegre & Pou, 2006; Fleischer & Pizam, 2002). Of these, the Tobit model was used in the former whereas the Logit model in the latter. Whenever a dependent variable takes either a positive value or is zero (these kinds of models are known as the limited dependent-variable models), Tobit model is used. On the other hand, if the dependent variable takes the value of one or zero, then the Logit model is used. Both models are estimated by the maximum likelihood method (Greene, 2000). The findings of these studies are consistent with each other to a great extent despite the fact that different models were employed and different destinations were considered in the sampling. For example, one consistency appears on the proposition that an increase in both age of visitors and annual household income leads to an increase in the duration of stay during their vacations. To the best of our knowledge, there exist no known published studies that have utilized survival analysis (also called duration analysis) to explain specific determinants of length of tourists’ vacations. Survival analysis is a fairly new area of interest, but a rapidly growing area in econometrics (Greene, 2000). When timing of a certain event is important in the investigation, survival analysis is used. These kinds of models have long been applied in technical fields such as industrial engineering and biomedical sciences, but their applications are limited in marketing and tourism research. Engineers are interested in the

ARTICLE IN PRESS 738

U. Gokovali et al. / Tourism Management 28 (2007) 736–746

life of various machines and the durability of their components. On the other hand, experts in biomedical sciences are interested in, for example, survival times of their patients such as heart transplant recipients and the length of survival times after a patient has received a certain kind of treatment (Kiefer, 1988). Albeit limited, these methods have been recently applied in social sciences as well, e.g., duration of strikes, length of unemployment, intervals between conception, time until business failure, length of time between arrests, length of time from purchase until a warranty claim is made and intervals between purchases (Greene, 2000, p. 937). Kiefer (1988, p. 648) reports a detailed list of its practical and potential applications in social sciences: duration of marriages, spacing of births, time to adoption of new technologies, time between trades in financial markets, product durability, geographic or occupational mobility (time between moves), lifetimes of firms, time to invention from research investment, payback periods for overseas loans, durations of wars, time in office for congressmen and other elected officials, time from initiation to resolution of legal cases, spacing of purchases of durable goods (or replacement capital), time in rank and length of stay in graduate schools. 2.1. Model development The above-mentioned list indicates that survival analysis in tourism is under-researched. When the length of stay is used as a dependent variable in standard regression models, some problems and difficulties may arise (Kiefer, 1988, p. 647). The first one is that values of exogenous variables may change during the stay and this creates conceptual problems in standard regression analysis (Kiefer, 1988, p. 647). These types of problems can be handled by conducting a survival analysis (under the heading of time varying-dependent-explanatory variables). However, in practice, the regressors may change only once or a few times over the course of the spell and can be analyzed by hazard rate formulation (Kiefer, 1988, p. 670). In usual settings, explanatory variables are time independent and specific to spell. Time-varying (dependent) covariates violate the assumption of independence conditionality. When time is modeled as in the case here, it is not expected to have negative fitted values. Thus, the error component of such a model will most probably have skewed the distribution than a symmetric one such as the normal distribution (Hosmer & Lemeshow, 1999), which validates the use of survival analysis. The other characteristic of the survival analysis consists of an ‘‘inherent aging process’’ when a tourist stays at a destination. The inherent aging process refers to the dependent variable under consideration (the length of tourist stay in this case), which should be assigned positive values. The fact that we use time as a dependent variable restricts the model that can be used. The positive aging process (positive length of stay) forces one to choose a

model in which the systematic component of the model must yield fitted values that are strictly positive. A linear regression model can yield negative fitted values, particularly for subjects with short survival times (short-term holidays), which makes linear regression unsuitable for these kind of data (for more detailed information, see Greene, 2000; Hosmer & Lemeshow, 1999; Kiefer, 1988). This characteristic of the data is what distinguishes survival time from other dependent variables (Hosmer & Lemeshow, 1999, p.87). With this device, the expected duration that is conditioned on a set of covariates can be characterized. The main interest in the survival analysis is not the unconditional probability of an event taking place, e.g., the probability of a tourist staying exactly 14 days at a destination. Rather, the interest is the conditional probability, e.g., the probability of a tourist’s intention to leave the destination in 14 days given that the tourist stayed 13 days. In this study, survival analysis, which is designed to explain the duration process, is used to determine the factors that affect the tourist stay in Turkey. In this case, the process to be modeled is the time it takes for a tourist to leave the country. Duration data (also called ‘‘spells’’) is expressed by a survival function or a hazard function. The length of spell can sometimes be unknown (‘‘censored’’). A censored observation refers to an incomplete value of observation. There are two types of censoring (Hosmer & Lemeshow, 1999): (a) left censored, if the event of interest has already occurred when observation begins and (b) right censored, if the observation begins at the defined time and terminates before the outcome of interest is observed or if the survival time of the event is not observed because of the end of the study. Our data to be analyzed are not any censored type. The most commonly encountered censoring mechanism is the one in which observation begins at the defined time (tourists arrive in Turkey for their holidays) and terminates before the outcome of interest (leaving Turkey at the end of their holidays) is observed. In this case, an incomplete nature of observation appears in the right tail of the time axis, known as right censored. In the data we utilized, right censoring did not arise because of the fact that the outcome of interest (tourists leaving Turkey) is observed. If we were unable to have information about the length of stay for various reasons, then right censoring would have occurred. As we surveyed only those passengers who completed their vacations and were about to fly back home, our event of interest (length of stay) is complete. Survival function is the probability of observing a survival time greater than or equal to some stated value (Hosmer & Lemeshow, 1999; Kiefer, 1988), whereas hazard function is the rate at which the spell will be completed at duration t, conditional upon that they last until t (Kiefer, 1988, p. 651). If the interest is the probability that the spell is of a length at least t, survival function is utilized. On the other hand, if the interest is the probability that the spell will end in the next short period,

ARTICLE IN PRESS U. Gokovali et al. / Tourism Management 28 (2007) 736–746

then the function to be used is the hazard rate specification. These two terms are related to each other. More formally, the approach may be described as follows. Spell length is represented by the random variable T (in our case it is the length of stay, measured in days) with a continuous probability distribution f(t) and t is a realization of T (from T ¼ 0 to T ¼ t). The cumulative distribution function for continuous distribution, F(t), is the probability that a random variable will have a survival time less than some stated value t; F(t) ¼ Pr(Tpt) and the corresponding density function is f(t) ¼ dF(t)/dt, whereas survival function is stated as S(t) ¼ 1F(t) ¼ Pr(TXt), i.e., the probability of observing a survival time greater than or equal to some stated value t (Hosmer & Lemeshow, 1999; Kiefer, 1988). Hazard function is defined in terms of conditional probability. It is the rate at which the spell will be completed at duration t, conditional upon that they last until t (Kiefer, 1988, p. 651): lðtÞ ¼ f ðtÞ=SðtÞ. The advantage of the hazard function is that it permits to address specific questions such as how the length of tourist stay is related to economic variables as well as other tourist characteristics. Two frequently used models for adjusting survival functions for the effects of covariates are the accelerated failure time model and proportional hazard (PH) ratio model. The commonly used specification is the PH ratio model, so this paper follows the same practice. In the PH ratio model, the covariates have a multiplicative effect on the hazard function: lðtj Þ ¼ l0 ðtÞgðxj Þ, where l0(t) is baseline hazard function and g(xj) is the nonnegative function of covariates. A popular choice and the one adopted here is to let g(xj) equal the relative risk, i.e., g(xj) ¼ exp(xjB), yielding lðtj Þ ¼ l0 ðtÞ expðbx1 þ bx2 þ    þ bxj Þ. In the hazard rate formulation, b represents the effects of increases in X on the conditional probability of a termination of a stay, whereas in the standard regression analysis b measures the effect of increases in X on the length of stay (Kiefer, 1988, p. 67). The function l0(t) can be left unspecified, yielding Cox’s PH model, or it takes a specific parametric form such as exponential, Weibull and Gompertz. Each has corresponding survival and hazard functions (for detailed analysis, see Kiefer, 1988). The hazard function is constant in exponential distribution, which implies that the risk of ‘‘failure’’ is the same, no matter how long the spell has been observed (Hosmer & Lemeshow, 1999). Due to these characteristics, Kiefer suggests that the exponential distribution is said to be memoryless, and if data contain both very short and long durations, exponential distribution would be inadequate (Kiefer, 1988, p. 653). The baseline hazard function in exponential distribution is l0(t) ¼ g. Weibull’s distribution

739

is a generalization of the exponential distribution and suitable for modeling data with monotonic hazard rates that either increase or decrease exponentially with time. The corresponding baseline hazard function is l0(t) ¼ gata1, with g40 and a40. When a ¼ 1, we get exponential distribution as a special case. Gompertz’s model has been used by medical researchers and biologists to model data on mortality. This model is suitable as in the case of Weibull’s model to model data with monotonic hazard rates that either increase or decrease exponentially with time. The corresponding baseline hazard function for Gompertz’s distribution is l0(t) ¼ exp(g+dt), with g40, d40. 2.2. Estimation techniques in duration analysis In estimating coefficients, parametric and semi-parametric techniques are used. After the duration distribution (exponential, Weibull’s or Gompertz’s) is specified for the data, parametric estimation is done by using the maximum likelihood estimation (MLE) method (Kiefer, 1988, p. 663). Kiefer also suggests that specification of the hazard function rather than densities in modeling the duration may be easier to interpret. Semi-parametric estimation used in the analysis utilizes Cox’s PH specification. Parametric estimation is done by MLE after constructing the loglikelihood function for Cox’s PH specification (for more detailed information, see Hosmer & Lemeshow, 1999; Kiefer, 1988). Three asymptotically equivalent tests, Wald, Score (or Lagrange Multiplier) and Likelihood Ratio can be used for the validity of regressors in the model. The decision on which one to be chosen depends on one’s convenience (Kiefer, 1988, p. 674). Wald test is used in our analysis. As a model selection tool, the Akaike Information Criteria (AIC) can be used to select the best-fitting model among PH ratio specifications. There is also another test that whether Cox’s PH assumption fits the data or not, hence this test is also carried out. 3. Methodology The model has been estimated with the data for a sunand-sea destination, Bodrum, in Turkey. As a tourist town of Mugla, Bodrum has been an internationally well-known destination over the last three decades. It has a lot to offer in tourism for its visitors, varying from culture to nature and from agriculture to entertainment. In addition to those originating from the Commonwealth of Independent States, OECD countries always take the first place in the table of the distribution of arrivals in Turkey by nationality. In particular reference to tourist-generating markets, Table 1 shows the distribution of major tourist markets by nationality, attracted to the Mugla Province from 1996 to 2005. As seen, Great Britain is the number one country accounting for the majority of foreign tourist arrivals in Mugla, recording a significant performance over the last two decades. Although Mugla relies on the British

ARTICLE IN PRESS

5.0 6.0 5.7 4.9 4.0 4.5 4.5 5.1 4.4 n.a. 14.3 15.1 18.1 17.1 15.8 17.3 16.5 15.5 14.9 14.2 5.7 7.1 7.3 7.1 6.1 6.0 5.2 6.5 5.3 n.a. Source: Republic of Turkey Ministry of Tourism, Bulletin of Accommodation Statistics 1996–2005, Ankara.

.7 1.0 1.1 1.2 1.3 2.1 2.5 2.4 2.8 2.4 5.7 5.9 7.7 6.7 5.2 6.1 5.4 7.4 5.9 n.a. 4.0 4.2 3.6 2.8 2.7 2.9 2.8 2.2 1.7 1.3 5.1 8.0 9.3 8.1 6.6 6.7 5.0 6.3 4.2 n.a. 4.2 4.6 3.5 4.5 6.3 6.3 5.6 5.4 5.4 5.6 7.1 6.6 6.0 7.6 3.2 5.6 6.0 6.7 6.3 n.a. .1 .1 .1 .2 .3 .5 .7 .8 .7 .5 6.9 6.1 7.0 6.8 5.9 5.9 5.2 5.9 4.7 n.a. 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Share in Turkey Average stay Average stay Share in Turkey Average stay

Share in Turkey

Average stay

Share in Turkey

Average stay

Share in Turkey

Average stay

Share in Turkey

Turkey total Mugla total Benelux Germany Great Britain CIS

Countries Years

Table 1 Distribution of tourists and average length of stay by the country (1996–2005)

100 100 100 100 100 100 100 100 100 100

U. Gokovali et al. / Tourism Management 28 (2007) 736–746

740

market, it also attracts a wide range of tourism demand from Germany, the former Soviet Union and the Benelux countries. While examining the average length of stay by nationality, there are not many variations in the length of stay by nationality, ranging between five and seven days. This figure has shown significant variations from one year to the next. That is to say, except for the Benelux countries, tourists from the remaining three countries decreased their average length of stay from 1996 to 2004. Overall, the average length of stay remains at a low level, varying between 4.5 and 6.0 days. One may speculate that the devaluation of Turkish Lira (TL) encouraged an increasing number of foreign tourist arrivals, but with lower length of stays. An increase in both the number of emerging destinations worldwide and vacations taken per year might have lead to a decrease in visitors’ intentions to stay. The literature refers to limited studies concerning tourists’ length of stay during their vacations abroad and the evaluation of their determinants at microeconomic level. The dependent variable is the hazard rate and the length of tourist stay is the event under consideration. In this formulation, the hazard rate is the conditional probability of termination of stay in Bodrum. As explanatory variables, tourists’ sociodemographic profiles and holiday characteristics as well as economic variables were used. The data were based on a questionnaire survey conducted among those who were about to complete their vacations and depart from the airport back home during the summer of 2005. With the assistance of two trained interviewers and the cooperation of the airport authority, a total of 1023 questionnaires were collected through the end of a three-week period. The survey was conducted only on those who came from the Netherlands, Germany, Britain and Russia. Each nationality was presented a copy of the questionnaire that was designed in their official language. Those staying in their own houses were excluded from the analysis, so we had 957 observations available for a further stage of data analysis. Table 2 gives sample characteristics of the data. British tourists constitute the highest share, followed by tourists from the Netherlands. Average number of days stayed is highest for German tourists and relatively the lowest for their British counterparts. As far as tour organizations are concerned, full package is the one preferred to partial package and independent travelers. Again, all-inclusive vacation is the most dominant type of holiday package among its alternatives for all nationalities, except for British tourists who prefer mostly bed and breakfast, selfcatering types of accommodation. In terms of daily spending, there appear potential differences among nationalities. The level of spending is the highest for Russian tourists and the lowest for German tourists. British tourists constitute the highest share in terms of skilled occupation and semi-skilled occupation, whereas the Dutch tourists constitute the highest share in unskilled occupation. As for the type of accommodation, a hotel is the most favored option followed by apart hotels, holiday villages and yachts, respectively.

ARTICLE IN PRESS U. Gokovali et al. / Tourism Management 28 (2007) 736–746

741

Table 2 Summary of the respondents’ profile Variables

Average length of stays Number of tourists taking full-package vacation Number of tourists taking partial-package vacation (flight and accommodation only) Number of independent tourists Number of tourists taking all-inclusive vacation Number of tourists taking half-board accommodation Number of tourists taking full-board accommodation Number of tourists demanding self-catering and bed and breakfast Daily spending (mean h) Number of tourists by skilled jobs Number of tourists by semi-skilled jobs Number of tourists by unskilled jobs Number of tourists staying at hotels Number of tourists staying at apart hotels Number of tourists staying at holiday villages Number of tourists staying on yachts (blue voyage) N Percentage of total

Countries CIS

Great Britain

Germany

Netherlands

Total

10.98 173 25

10.69 217 51

11.95 149 15

10.88 112 107

11.06 651 198

11 160 25 20 4

37 80 49 6 170

30 130 25 6 33

30 141 38 2 68

108 511 137 34 275

120.89 107 25 77 184 11 14 0 209 21.84

117.47 185 45 75 165 106 27 6 305 31.87

72.43 88 24 82 146 11 27 9 194 20.27

77.85 96 17 136 166 57 23 3 249 26.02

98.95 476 111 370 661 185 91 18 957 100

4. Discussion of findings As possible explanatory variables on the length of stay which is used as a reference variable, semi-demographic profiles of tourists and their holiday characteristics are utilized. Several models were estimated by Cox’s semiparametric method and the exponential, Weibull’s and Gompertz’s parametric techniques. A statistical test was undertaken to check whether Cox’s PH assumption fits the data or not. The most important assumption of Cox’s PH specification is that the hazard ratio is proportional over time. Test of Cox’s PH assumption is based on the scaled Schoenfeld residuals (Hosmer & Lemeshow, 1999). The results indicate that Cox’s PH assumption has been accepted (degrees of freedom ¼ 39; w2 ¼ 33.20; p ¼ .7309). Among the three parametric survival models, i.e., exponential, Weibull’s and Gompertz’s, to find the best fitting model, AIC values are calculated. (AIC ¼ 2(loglikelihood)+2(c+p+1), where c is the number of model covariates excluding constant and p is the number of specific ancillary parameters. It is zero for exponential distribution and equals to one for Weibull’s and Gompertz’s distributions). The decision rule is such that the smallest AIC value gives the best-fitting model. Table 3 gives AIC values. Our results indicate that Weibull’s distribution has the smallest AIC value. The exponential and Gompertz’s models are eliminated by this criterion; thus, the results of exponential and Gompertz’s specification are not reported here. Only the results of Weibull’s and Cox’s specifications are reported and discussed, because Cox’s PH assumption is not rejected and Weibull parametric estimation gives the smallest AIC value.

Table 3 AIC and log likelihood values Parametric models

Log-likelihood values AIC values

Exponential

Weibull

Gompertz

698.76022

113.66741

188.92712

1477.52044

309.33482

459.85424

Note: AIC ¼ 2(log-likelihood)+2(c+p+1) where c is the number of covariates excluding constant, p is the number of specific ancillary parameters. It is zero for exponential distribution and equals to one for Weibull’s and Gompertz’s distribution.

Table 4 reports the results of Cox’s and Weibull’s regressions. Observations (n ¼ 957) with missing information for any of the variables were dropped from a further analysis. Thus, the sample which the regressions are based on consisted of 672 observations in all. In survival analysis, the interpretation of results is different from that of a conventional linear regression. The coefficients of variables give the effect of an increase in explanatory variables on the conditional probability of ending a tourist stay. A negative sign means that as the value of a variable increases, the hazard rate of tourists’ duration decreases or the survival of their duration increases. A positive sign indicates that an increase in explanatory variables has a decreasing impact over the length of stay. A summary of significant results with both positive and negative signs is provided in Table 5.

ARTICLE IN PRESS 742

U. Gokovali et al. / Tourism Management 28 (2007) 736–746 Table 4 (continued )

Table 4 Estimation results of Cox’s and Weibull’s regressions Variables

Cox’s regression

Constant British tourists

Variables Weibull’s regression 9.474969 .5745851

.2427624*** (.0983918)

.3845674*** .1257495

Russian tourists

.407698*** .1261569

German tourists

.0299351 .1090062

.075917 .1379288

Age1: 18–24 years

.1449175 .1286485

.1417495 .1596998

Age2: 25–34 years

.2024447* .1153323

.2157695 .1464883

.6790261*** .1551241

Cox’s regression

Weibull’s regression

Number of people in the party (travel companions)

.0513923* .0290109

.0564015 .0371397

Number of past visits to the destination

.0492151* .0300563

.092149** .0424621

Number of past visits to Turkey

.0323118 .0209337

.0455036* .0265843

Average daily spending (mean h) Quality of services and facilities Level of hospitality

.0019587*** .0003559 .0847622 .0711986

.0026295*** .00047 .1628107* .0935977

.2205858*** .0683558

.3724078*** .0898498

Attractiveness of natural and cultural environment

.1300442*** .0442961

.1528488*** .0573313

Standard of nightlife and entertainment

.0546808* .0321339

.0650596* .0397241

Age4: 45–54 years

.0132586 .0795681

.0296283 .1030976

Age5: 55–64 years

.0667673 .1495281

.090781 .1868809

Standard of accommodation facilities

Age6: 65 years and over

.0575616 .2822145

.0530275 .35184

.0354535 .0513273

.0919997 .0637634

Standard of safety and security

Number of tourists by skilled jobs

.1545899 .0992057

.2946553** .1404697

.0206148** .0097833

.0160558 .0105494

Quality of sea and beaches

Number of tourists by unskilled jobs

.1197523 .107159

.3139178** .1502165

.0077025 .0382826

.0151999 .0513473

Distance to home country

Level of education

.0619889* .0337463

.1004113** .0424533

.0118253 .0275211

.0300976 .0353312

Marital status

.0216411 .0954985

.0631427 .123212

Overall attractiveness and image of Turkey

.0958335** .0430616

.1311054** .0565303

Effectiveness of the promotion and publicity

.0732965* .0381989

.0813948 .0503246

Recommendation of friends and colleagues

.0498931 .0312411

.0902515** .0410755

Level of annual household income (in h)

.0395339** .0177331

.0626163*** .0221406

Experience as an international tourist

.0633043** .0288987

.0764131** .0361569

Number of vacations taken abroad

.0615111** .0268268

.0944546*** .0334399

Number of tourists taking all-inclusive vacation

.3108343*** .0998661

.4696107*** .1417116

Number of tourists taking full-board accommodation

.138018 .2040956

.0648627 .2620033

Number of tourists demanding selfcatering and bed and breakfast

.0028966 .1137063

.0718783 .1638862

Number of tourists demanding a fullpackage vacation

.1539496 .0958241

.0950685 .1184804

Number of tourists demanding not a package vacation (independent tourists)

.2643953** .1291248

.4029075** .1901151

How far in advance the reservation was complete

.1756385*** .0282897

.2320268*** .036721

Number of tourists staying at hotels

.0520291 .0947152

.0541922 .1223277

Number of tourists staying at holiday villages

.0721147 .1294515

.1151442 .1770573

Number of tourists staying at yachts

1.581247*** .2792993

2.432873*** .3378154

Wald w2 (39)

257.51***

301.17***

Figures in parenthesis are robust standard errors. *, ** and *** refer to the significance level at 10%, 5% and 1%, respectively.

In general, Both Cox’s and Weibull’s regressions yield similar results for most coefficients in the regression. Nationality has a strong influence on the length of stay. Coefficients on British tourists in both regressions are positive and significant, which means that their survival probability to stay is less than that of those from the Netherlands, which is taken as a base dummy variable. On the other hand, Russian tourists have a comparatively larger probability of staying longer than their counterparts from the Netherlands. Coefficients on German tourists are not significant in either regression model, i.e., there is no significant difference on the probability of staying longer (or shorter) between German and Dutch tourists. This result may be subject to higher standard errors compared to coefficient values. Indeed, German tourists have a mean value of 11.95 days with a standard

ARTICLE IN PRESS U. Gokovali et al. / Tourism Management 28 (2007) 736–746 Table 5 Summary of robust research findings Propositions

Weibull’s regression

Cox’s regression

Increase in variables/status

Increase in probability of staying +  + +

Increase in probability of staying +  + +

+

+

+ + +

+ + +

+

+

+

+

  

  

  

  

Nationality (Russian tourists) Nationality (British tourists) Annual household income (in h) Experience as an international tourist Non-packaged vacation (independent tour) Reservation in advance Past visits to the destination Attractiveness of natural and cultural environment Standard of nightlife and entertainment Overall attractiveness and image of Turkey Level of education Average daily spending (in h) Number of vacations taken abroad per year Type of vacation (all-inclusive) Type of accommodation (yacht) Level of local hospitality

deviation of 4.3 days, which is much higher than that of any other sample groups. This can be the case if there is much variation in terms of the length of stay among German tourists yielding higher standard deviations. The length of stay ranges between 2 and 35 days for German tourists, which shows higher variation compared to that for tourists from other countries. Among tourist groups, Russian tourists have a higher inclination to stay longer. There seems to be no statistical evidence that the age variable correlates with the length of stay except that, in Cox’s regression, age2 is significant at 10% level. This indicates that people aged between 25 and 34 years have less probability to stay longer compared to people aged between 35 and 44 years, which is taken as a base dummy. Although there is no indication that occupation matters for the length of stay in Cox’s regression, the contrary evidence is found from Weibull’s regression. Coefficients for both skilled and unskilled occupations are positive and significant, which means that people who have skilled and unskilled jobs have a less probability of staying longer compared to those with semi-skilled jobs. Coefficients on the education variable are positive and significant in both regressions. Thus, as respondents’ education level increases, probability of staying longer decreases. There is no evidence that marital status matters for the length of stay. Coefficients on income and experience are significant and have negative signs. As tourists’ annual income increases, the probability to stay longer increases. The

743

coefficient of experience in international tourism has a negative sign and is significantly different from zero. This finding indicates that as experience in international tourism increases, the probability of staying longer increases. On the other hand, as expected, as tourists plan to have more than one holiday abroad in 2005, their probability of staying longer decreases. Regarding the evaluation of the relationship between respondents’ patterns of taking holidays and their tendency to stay, various results were obtained. Those who prefer an ‘‘all-inclusive’’ type of holiday tend to have a lesser probability of staying longer than those choosing a ‘‘halfboard’’ accommodation. No significant results are found for the coefficients of the ‘‘full-board’’ and ‘‘other type of holiday’’ variables, namely, self-catering and bed and breakfast. Independent tourists have a higher probability of staying longer compared with those who come with a partial-package tour demanding either ‘‘charter flights’’ or ‘‘accommodation only’’. The coefficients of the ‘‘full package’’ variable are not significant in any regressions. The coefficient of the reservation variable is negative and significant in both regressions. Thus, late reservations are associated to a shorter length of stay. The coefficients of the types of accommodation are only significant for the yacht variable: it has a positive sign and is significant, which indicates that those staying in yachts have a lesser probability of staying longer than those staying in apart hotels. Cox’s results justify that as the number of people in the party increases (travel party composition), the probability of staying will be longer. However, this result is not confirmed by Weibull’s regression. As the number of previous visits to the destination increases, the probability of staying longer also increases. This statement is supported by both regressions. As the number of previous experiences in Turkey increases, the probability of staying longer increases as well, but this is confirmed only by Weibull’s regression. As expected, the sign on the daily spending variable is positive and significant, which indicates that as the amount of tourists’ daily spending increases, their probability of staying longer decreases. As for the evaluation of the findings on the basis of holiday characteristics at the destination (supply side), coefficients of attractiveness, entertainment and overall image of the country have negative sign and are significant in both regressions. These holiday characteristics have a significant influence on the length of stay. The coefficient of the quality variable is negative and significant in Weibull’s regression, whereas safety and security variables are negative and significant in Cox’s regression; this indicates that these holiday characteristics may also have an influence on the length of stay. The sign of hospitality is somewhat paradoxical, which means that tourists are reluctant to pay attention to this variable while making decision on how long to stay. In Cox’s regression, it is found that as the effectiveness of promotion increases, the probability of staying longer decreases. This result implies

ARTICLE IN PRESS 744

U. Gokovali et al. / Tourism Management 28 (2007) 736–746

that Bodrum is promoted as a short-period destination; however, this result does not seem to be a robust one, as it is not confirmed by Weibull’s regression and it is significant only at 10% level in Cox’s regression. Coefficient on the word-of-mouth recommendation is positive and significant in Weibull’s regression, but this result is also not supported by Cox’s regression. 5. Conclusions and implications This paper aimed at investigating the significant determinants of tourists’ length of stay by utilizing a questionnaire survey conducted among a tourist group of four nationalities in the summer of 2005. Factors related to both supply and demand were included in the model as independent variables. A wholly different approach in tourism research, albeit commonly used in other fields such as economics, was employed, while determining the power of various factors over tourists’ sensitivity of the length of their stays at a sea-sun-sand-dominated vacation destination. Of several estimation methods utilized, semi-parametric Cox’s regression and Weibull’s parametric estimation were chosen, according to diagnostic tests. This paper has examined some of the evidence regarding ways in which the decision to stay for a certain length of time is influenced by various sociodemographic and holiday-based factors. To summarize, this study has confirmed a strong support for a positive relationship for variables such as nationality (for Russian tourists), income, experience, independent tour, timing of reservation and familiarity. A negative robust relationship has appeared for the following variables: level of education, level of daily spending, number of annual vacation plans, type of holiday (all inclusive) and nationality (for British tourists). The variable yacht as a type of accommodation is also inversely related to the length of stay. The careful assessment of these findings is also worth noting for the evaluation of market segmentation studies by the length of stay in tourism research. One may use these findings depending upon their preferences to attract client segments staying either for a longer or a shorter period. For example, the Russian market would be an attractive one when the length of their stays is taken into account. However, one may also argue that it should not be a logical approach if one may consider a subsequent finding underlining that those with higher spending tend to stay shorter. The destination loyalty also has an influential power to stay longer. This result is especially important for setting policies; so policies supporting the destination loyalty would increase the length of stay, but may lead to a lower level of tourist spending; thereby economic benefits cannot be maximized. The length of stay is a priori stage for potential tourists while making a decision to go on a vacation and choosing a place to stay. As such, one of the existing problems in the tourism industry is the length of stay. The literature suggests that different factors contribute towards reduction in the length of stay, including changes in tourists’ habits

such as a preference to go on more than one vacation per year (Alegre & Pou, 2006). This proposition is also supported by the findings of this current study. Although those traveling more than once per year are seen as an emerging trend as a consequence of new consumer characteristics in international tourism, strategies to attract those traveling just once a year might be developed if they bring more benefits to the destination. In addition, the study findings were also consistent with those of other similar studies conducted previously and confirmed that income has a positive influence on the length of stay (Fleischer & Pizam, 2002). According to the rule of basic economics theory, as income rises, demand for luxury goods is expected to increase in a greater proportion (Varian, 1990). As the demand for tourism is viewed as luxury consumption (which means not a utilitarian consumption to satisfy basic human needs), then increase in per capita income results in an increase in tourism demand (Akis, 1998; Croes & Vanegas, 2005; Dritsakis, 2004; Song, Wong, & Chon, 2003). Taking this argument as a reference point, one can assume that those with their higher annual income tend to stay longer. That is, there exists a direct relationship between the level of annual household income and the length of stay, where the latter is expected to rise as the former increases, but this does not necessarily mean that the average daily spending will also increase. The relationship between the familiarity and the probability of staying longer is a further implication of this study. One series of studies suggests that familiarity dominates one’s propensity to visit the destination (Bargeman & van der Poel, 2006; Court & Lupton, 1997), their overall trip satisfaction (Kozak & Rimmington, 2000), the level of their spending (Perez & Sampol, 2000) and activity participation (Lehto, O’Leary, & Morrison, 2004). In terms of the influence of supply-based factors, attractiveness of natural and cultural environment, standard of nightlife and entertainment, and overall attractiveness and image of the country stimulate the length of stay. Evidence suggests that tourists tend to give utmost consideration for inclusion of such objective and subjective offers as sources, facilities and image in their agenda while deciding the duration of their vacation time. Promotion of these holiday characteristics would increase the length of stay and thereby assist service providers to gain more economic benefits on the condition that the level of daily spending continues to make a contribution to this trend at a desired level. However, contrary to our expectations, the coefficient of the local’s hospitality has a negative impact. This item seems to become an influential negative factor while tourists make plans about their length of stays; although in earlier studies, it correlated highly as a significant positive factor with people’s motivations and their overall trip satisfaction (Kozak, 2001). In other words, a better perception of hospitality leads to taking a shorter vacation. From a pessimistic point of view, this finding could be used as a marketing strategy to attract those visitors with the intention of staying shorter.

ARTICLE IN PRESS U. Gokovali et al. / Tourism Management 28 (2007) 736–746

The study findings suggest no compelling robust evidence that deliberate attempts to forecast the relationship for age, occupation, marital status, type of holiday (full-board versus half-board and other type of holiday versus half-board), full package (versus partial package), type of accommodation (hotels versus apart hotels and holiday villages versus apart hotels), travel party composition, number of previous visits to the country. In terms of supply-based factors, there is no robust evidence that the length of stay significantly correlates with the quality of services and facilities, standard of accommodation facilities, standard of safety and security, quality of sea and beach, distance to home country, effectiveness of the promotion and publicity, and the word-of-mouth recommendation of friends and relatives. It is interesting to report that the recommendation of friends and relatives, in spite of its contribution on destination choice (Mill & Morrison, 1992; Sirakaya & Woodside, 2005), has no impact on the length of stay. Several limitations are evident and require comments. Although other studies (Fleischer & Pizam, 2002) confirmed that elderly people tend to stay longer, our study findings do not support this proposition. Our estimation results do not give robust results in terms of the coefficients of the age variable due to timing of the survey. As known, these (elderly) people usually take their vacations earlier or later than summer months. This survey was carried out in the middle of summer season. Moreover, several destinations (e.g., Bodrum in this case) are best known for their nightlife and entertainment, which is heavily dependent on young and middle-aged clients. Hence, this can be considered as a further reason to explain as to why robust evidence does not exist for the age variable. As a result, these kinds of surveys should be conducted at different times of the summer season, so that effects of the variation among the timing of vacations could be observed. In doing so, the diversification of different age groups in terms of determinants of their length of stays could be investigated. Finally, this sample was composed of those visiting only one destination so that the generalizability of these findings is limited to similar populations. One avenue for future research would be to repeat this study on different people visiting different destinations either in Turkey or in other countries to make comparisons among them in terms of the determinants of length of stay. As for implications of the theory, the category of data analysis applied in this study differs from its counterparts in several aspects. First, the methodology utilized is entirely different from other similar studies (Alegre & Pou, 2006; Fleischer & Pizam, 2002). In this study, length of stay of tourists is formalized in terms of the hazard rate, which gives the probability of termination of staying at time t knowing that it was in force until t. Many distributions would be applicable to the data. Hence, best fits will be given among several alternatives. To our best knowledge, as survival analysis is a new topic in social sciences; its application in tourism research has been underinvestigated.

745

Thus, this study is among the first of its type to use survival analysis to investigate the main determinants of the length to stay on a pleasure vacation. Yet much remains to be explored about such relationships. Given the limited amount of studies dealing with the decision to stay and its importance from the perspective of marketing implications, this seems a worthwhile topic for a continued investigation. References Akis, S. (1998). A compact econometric model of tourism demand for Turkey. Tourism Management, 19(1), 99–102. Alegre, J., & Pou, L. (2006). The length of stay in the demand for tourism. Tourism Management, in press. Bargeman, B., & van der Poel, H. (2006). The role of routines in the vacation decision-making process of Dutch vacationers. Tourism Management, in press. Court, B., & Lupton, R. A. (1997). Customer portfolio development: Modeling destination adopters, inactives and rejecters. Journal of Travel Research, Summer, 35–43. Croes, R. R., & Vanegas, M. V. (2005). An econometric study of tourist arrivals in Aruba and its implications. Tourism Management, 26(6), 879–890. Davies, B., & Mangan, J. (1992). Family expenditure on hotels and holidays. Annals of Tourism Research, 19, 691–699. Decrop, A., & Snelders, D. (2004). Planning the summer vacation: An adaptable and opportunistic process. Annals of Tourism Research, 31(4), 1008–1030. Dritsakis, N. (2004). Cointegration analysis of German and British tourism demand for Greece. Tourism Management, 25(1), 111–119. Fesenmaier, D., & Jeng, J. (2000). Assessing structure in the pleasure trip planning process. Tourism Management, 5, 13–17. Fleischer, A., & Pizam, A. (2002). Tourism constraints among Israeli seniors. Annals of Tourism Research, 29(1), 106–123. Greene, W. (2000). Econometric analysis. Englewood Cliffs, NJ: PrenticeHall. Hosmer, D., & Lemeshow, S. (1999). Applied survival analysis. New York: Wiley. Judith, C. (1999). Hotel online—Special report. http://www.ic.gc.ca (Accessed 18.01.06). Kiefer, N. (1988). Economic duration data and hazard functions. Journal of Economic Literature, 26, 646–667. Kozak, M. (2001). Repeaters’ behavior at two distinct destinations. Annals of Tourism Research, 28(3), 785–808. Kozak, M. (2004). Destination benchmarking: Concepts, practices and operations. Oxon: CAB International. Kozak, M., & Rimmington, M. (2000). Tourist satisfaction with Mallorca, Spain, as an off-season holiday destination. Journal of Travel Research, 39(3), 260–269. Laws, E. (1995). Tourist destination management: Issues, analysis and policies. New York: Routledge. Legoherel, P. (1998). Toward a market segmentation of the tourism trade: Expenditure levels and consumer behavior instability. Journal of Travel and Tourism Marketing, 7(3), 19–39. Lehto, X. Y., O’Leary, J. T., & Morrison, A. M. (2004). The effect of prior experience on vacation behaviour. Annals of Tourism Research, 31(4), 801–818. Mak, J., Moncur, J., & Yonamine, D. (1977). How or how not measure visitor expenditures. Journal of Travel Research, 16, 1–4. Mill, R. C., & Morrison, A. M. (1992). The tourism system: An introductory text (Second Ed.). Englewood Cliffs, NJ: Prentice-Hall. Mok, C., & Iverson, T. J. (2000). Expenditure-based segmentation: Taiwanese tourists to Guam. Tourism Management, 21(3), 299–305. Morrison, A. M. (1989). Hospitality and travel marketing. New York: Albany.

ARTICLE IN PRESS 746

U. Gokovali et al. / Tourism Management 28 (2007) 736–746

Moutinho, L. (1987). Consumer behaviour in tourism. European Journal of Marketing, 21(10), 2–44. Mules, T. (1998). Decomposition of Australian tourist expenditure. Tourism Management, 19(3), 267–271. Oliver, R. L. (1999). Whence consumer loyalty. Journal of Marketing, 63, 33–44. Perez, E. A., & Sampol, C. J. (2000). Tourist expenditure for mass tourism markets. Annals of Tourism Research, 27(3), 624–637. Sirakaya, E., & Woodside, A. G. (2005). Building and testing theories of decision making by travellers. Tourism Management, 26(6), 815–832. Song, H., Wong, K. K. F., & Chon, K. K. S. (2003). Modelling and forecasting the demand for Hong Kong tourism. International Journal of Hospitality Management, 22(4), 435–451.

Spotts, D. M., & Mahoney, E. M. (1991). Segmenting visitors to a destination region based on the volume of their expenditures. Journal of Travel Research, 29(Spring), 24–31. Stevens, B. F. (1992). Price value perceptions of travelers. Journal of Travel Research, 31, 44–48. Um, S., & Crompton, J. L. (1990). Attitude determinants in tourism destination choice. Annals of Tourism Research, 17, 432–448. Varian, H. R. (1990). Intermediate Microeconomics. London: Norton. Witt, S. F. (1980). An abstract mode—Abstract (destination) node model of foreign holiday demand. Applied Economics, 12(2), 163–180. Woodside, A. G., & Lysonski, S. (1989). A general model of traveler destination choice. Journal of Travel Research, 27(1), 8–14.