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Segmentation of Visitors to a Heritage Site Using High-resolution Time-space Data Anat Tchetchik, Aliza Fleischer and Noam Shoval Journal of Travel Research 2009 48: 216 originally published online 13 March 2009 DOI: 10.1177/0047287509332307 The online version of this article can be found at: http://jtr.sagepub.com/content/48/2/216

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Segmentation of Visitors to a Heritage Site Using High-resolution Time-space Data

Journal of Travel Research Volume 48 Number 2 November 2009 216-229 © 2009 Sage Publications 10.1177/0047287509332307 http://jtr.sagepub.com hosted at http://online.sagepub.com

Anat Tchetchik Aliza Fleischer Noam Shoval Hebrew University of Jerusalem, Israel A novel method of data collection based on high-resolution time-space data recorded by global positioning system units was used to segment visitors to the Old City of Acre heritage site in Israel. This technique of accurately tracking the temporal and spatial behavior of visitors carrying the global positioning system units overcomes the well-known limitations of traditional data collection methods. Discrete-choice methods and a system of censored regressions were used to identify the decision-making process at each stage of the visit. The segmentation of the visitors was based on these decisions. It was found that different attributes associate the visitors with different segments at each level. Although the findings might be site specific, the data collection and the segmentation method could be used by tourism planners and decision makers at other sites. Keywords: GPS; segmentation; heritage tourism; choice modeling; Acre Israel

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ultural, including heritage, tourism has been growing rapidly in recent years (Alzua, O’leary, and Morrison 1998). It has been recognized in the literature that visitors to cultural tourism sites are often motivated to travel for different reasons than other types of tourists (DKS 1999; Formica and Uysal 1998; Hannabus 1999; Richards 1996a; Silberberg 1995; WTO 1985). Whereas most of the early research about the cultural tourism market (Hewison 1987; Richards 1996b; Richards and Bonink 1995; Miller 1997; Kemmerling-Clack 1999) tends to treat it as a homogeneous market, a growing body of empirical and conceptual work (Stebbins 1996; Timothy 1997; McKercher et al. 2002, among others) indicates that cultural tourists can be differentiated. Each destination has its own unique mix of cultural tourism products that might appeal to different types of tourists in different ways. Accordingly, Prentice (2001) and Richards (1996a) argued that more effort should be directed to exploring visitors’ behavior to particular attractions rather than studying overall demand for a general type of tourism. In this view, current work was undertaken to perform market segmentation of visitors to the Old City of Acre, Israel, which was declared a World Heritage Center by UNESCO in 2001. The segmentation concept offered in this study follows Dolnicar’s (2004a) concept 3 segmentation, that is, one a priori segmentation followed by another a priori

segmentation. The starting point is subgrouping of the tourist population, particularly visitors to a specific heritage site. Next, this segment is further grouped into subsegments by other a priori criteria. The criterion on which the segmentation is carried out is the visitors’ behavior on site, in particular, their choices at the different stages of the visit. The novelty of this article derives from the database on which it is based; a combination of high-resolution time-space data collected by global positioning system (GPS) and traditional questionnaires. Tracking tourists’ behavior at destinations using GPS devices is a relatively new method of data collection (Shoval and Isaacson 2007b), and Acre is one of the first places where this concept was fully utilized. Shoval and Issacson (2007a) introduced the method of sequence alignment as a tool for analyzing the sequential aspects within the temporal and spatial dimensions of human activities. This was done by implementing a method from biochemistry for spatial analysis of the high-resolution data obtained by the GPS devices. Shoval (2008) described the potential of the aggregative data of all the visitors who were tracked by GPS devices to better understand the impact of visitors on cities. This article uses the same highresolution time-space database that was obtained by GPS devices in Acre, but for the purpose of segmenting the visitors, an analysis that was not done previously. Using

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Tchetchik et al. / Segmentation of Visitors 217

GPS data enable the accurate recording of the temporal and spatial behavior emerging from decisions taken by the visitors during their visit, that is, which attractions they visit on site and how long they attend to each attraction. Providing objective information that is not biased by the visitors’ perceptions, this method overcomes the usual limitations of data collection in such sites due to the fact that tourists are not always aware and often do not remember exactly which attractions they visited and how long they stayed at each one. These limitations are demonstrated in Asensio, Garcia, and Pol (1993). Using both questionnaire data and direct observations, they found that more than half of the visitors to an exhibition in Velazquez Palace in Spain reported staying significantly longer than the actual time as observed by the researchers. Accounting for that and other problems, Espelt and Benito (2006), in a study of visitors to the Old Quarter of the city of Girona, Spain, combined direct observations with questionnaires. Tech nically, a research team followed the tourists unnoticed through their visit and recorded their behavior. When comparing the data gathered from observations to the data collected through declarations, significant differences were observed. The current study elaborates on Espelt and Benito’s (2006) by using GPS data, which are much more accurate and unbiased rather than direct observations, combining these data with questionnaires data. These data are employed to conduct visitors’ segmentation to Acre’s Old City based on the visitors’ decision-making process at the different stages of the visit. The segmentation method used here follows the literature analyzing the decision-making process of the consumer. This decision making can be looked on as a multistage contingent process as suggested by Jeng and Fesenmaier (2002). They proposed a three-stage travelplanning process of travelers. The first level of the decisions is the core one, and it is followed by secondary and peripheral decisions. The decisions at each stage are determined by the attributes of the travelers. Loomis (1995) similarly proposed four decisions a visitor to a recreation site must make: whether to participate at all in a given recreation activity, what sites to visit, how frequently to visit, and the length of stay at the chosen sites. By grouping the visitors a priori according to their decisions during the visit, it is found that different attributes of the visitors determine their group membership and that visitors can be regrouped differently at each stage. Moreover, in this process of differential visitors’ segmentation during the different decision stages of the visit, we identify complementarities and substitution patterns among the site’s different attractions. In general,

any two attractions can be considered complementary if the consumers usually consume them together or considered substitutes if they satisfy the same needs and are consumed one instead of the other. The segmentation results may help local tourism authorities and/or site managers to better comprehend the behavior and needs of the visitors. In a climate of an increasing number of heritage sites’ competing for “diminishing slices of a relatively static pie” (Apostolakis and Jaffry 2005), understanding visitors’ preferences may facilitates the design of products that best match them. This results in an increase in consumers’ satisfaction, the probability of repeated purchase, word-of-mouth promotion, and thus, overall sales (Dolnicar 2004b). Marketing agencies can apply the results of this analysis to develop strategies to target each market segment more effectively. Furthermore, an understanding of the linkages between attractions will facilitate marketing efforts. The rest of the article is organized as follows. The next section provides a literature review. The third section describes the Old City of Acre. The fourth section depicts the segmentation methodologies. The data collection and sampling method are given in the fifth section. The results of the segmentation are described in the sixth section. The last section summarizes and draws conclusions.

Literature Review Segmentation of cultural and heritage tourists has been the topic of numerous studies, both empirical and conceptual. Works performing conceptual segmentations include those of Silberberg (1995), who suggested segmenting tourists to cultural destinations by the extent to which culture provides the motivation for the visit; Stebbins (1996), who distinguished between specialized, generalized, and amateur cultural tourists; and Richards (1996b), who distinguished between “specific cultural tourists” and “general cultural tourists”; the former are among the heaviest consumers of heritage attractions while the latter are only occasional consumers. Timothy (1997) identified different levels of heritage tourism experiences based on the connectivity of the visitor to the site, that is, what the site symbolizes to him or her in terms of world, national, local, or private heritage. The empirical studies include the following:1 Formica and Uysal (1998) segmented visitors to cultural-historical events in Italy based on their motivations, emphasizing the importance of sociodemographic characteristics in defining the segments. DKS (1999) segmented heritage visitors to Pennsylvania as core, moderate, or low heritage


218 Journal of Travel Research

travelers, based on the importance of heritage in their choice to visit the destination, with each segment demonstrating different behavior. Chen, Kerstetter, and Graefe (2001) segmented visitors to industrial heritage sites in southwestern Pennsylvania according to the main reason influencing their decision to visit. They found that tourists’ reasons for visiting industrial heritage sites differed depending on travel experience, gender, and time of travel. Richards (2002) studied the concept of motivation as a driver to the behavior of visitors to cultural attractions and emphasized the importance of sociodemographics in forming motivation-based segments. Poria, Butler, and Airey (2003) offered the extent to which the site’s heritage is perceived as part of the tourists’ own heritage as a criterion to segment heritage tourists to the Western Wall in Jerusalem. They found that visitors who grasp the site as part of their own heritage were likely to demonstrate a higher satisfaction level and a longer stay than those who did not. Chang (2006) segmented tourists to aboriginal cultural festivals using tourists’ motives as the segmentation criteria. He found that motivational variables are more important than demographic variables in segmenting tourists to heritage sites. Other studies use a different approach: they allow the stated benefits or the actual behavior of the tourists reveal the differences between the segments. Prentice, Witt, and Hamer (1998) chose visitors to the Rhondda Heritage Park as the starting point for their data-driven research based on consumers’ experiences and benefits accumulated through the visit. They applied a combination of experience from the visit and benefit factors as a basis to segment visitors in terms of similarities in their experiences and benefits gained while visiting the site. Kerstetter, Confer, and Bricker (1998) segmented visitors to industrial heritage sites according to the types of attractions they visited. They found that the visitors’ behavior characteristics (such as the number of people traveling, the timing of their decision to visit, etc.) determine their assignment to the different segments. McIntosh and Prentice (1999) provided insights into the beneficial experiences the tourist gains when visiting heritage sites. They segmented tourists to three British period theme parks according to the perceived authenticity gained by the tourists. They demonstrated the relationship between the perceived authenticity gained and the tourists’ experiential and emotive processes in their interaction with attraction settings. Goulding (1999) identified three segments of visitors to an industrial heritage park according to their behavior, specifically (1) aesthetic behavior, visitors who are concerned about authenticity and have a romantic association with the past; (2) existential behavior, those alienated in the

present and who seek meaning and temporary control in the past; and (3) social behavior, those who use the park as a day out. McKercher (2002) segmented cultural tourists based on both psychographics (centrality of the cultural tourism as a trip motive) and depth of experience (in particular: superficial, shallow, and deep) dimensions and identified accordingly five market segments. These segments were then tested empirically applying cultural tourism to Hong Kong as a case study. McKercher and du Cros (2003) empirically tested the validity of the previous study’s five segments against a wider range of variables (demographic, experimental, attitudinal, and learning). McKercher et al. (2002) conducted a segmentation of cultural tourists to Hong Kong based on their choice of clusters of activities during the visit and then characterized the visitors according to socioeconomic, demographic, motivation, and other variables. Espelt and Benito (2006), drawing on both question naires and direct observations of visitors, segmented visitors to the monumental city of Girona, Spain, according to their “effective” behavior (number of sights, time spent at the sights, total time of visit, etc.). Applying a data-driven segmentation, they identified four segments: (1) noncultural tourists, who demonstrate a very short itinerary; (2) ritual tourists, who follow a kind of canonical pattern; (3) interested tourists, who visit more nodes with greater time spent in the nodes; and (4) erudite tourists, who are real cultural tourists. While sociodemo graphic variables did not influence the assignment of a visitor to the different clusters, the patterns of visitation did have an influence (e.g., the number of companions, guided visit, etc.). Prentice and Andersen (2007) segmented visitors to a preindustrial urban heritage museum in Denmark according to their consumption of the essentialisms of local, national, or generic culture that the site proffers (consuming is described as both experiential and empathetic). They identified two segments: (1) “re-iterative,” whose consump tion is characterized by an “imaginative recreation through temporary symbolic bonding with felt origins, personal, national, or generic,” and (2) “make-believer,” whose consumption is characterized by “facilitating imaginative fun as recreation.” In the aforementioned empirical studies, the common methodology is to administer questionnaire-based surveys in which visitors are asked to answer either open- or closed-ended questions, to rate statements reflecting their levels of experience and benefits (on a Likert-type or other scale), or to report their activities and other visitation patterns, their motivation, and perceptions depending on the segmentation criteria. Then assuming a posteriori



segmentation is conducted, segments are formed using methods such as cluster analysis or factor-cluster analysis, with the latter method dominating the data-driven segmentation studies in tourism research (Dolnicar 2008). Alternatively, if an a priori approach is being taken, the differences between the segments are statistically tested by comparing the means of the visitors’ attributes. This review of the literature demonstrates the variety of cultural-heritage tourism segmentation studies. These studies differ in many aspects, particularly: (1) the segmentation concept, whether a priori or data driven (Dolnicar 2004a); (2) the criteria applied for the segmentation (e.g., sociodemographics, psychographics, behavioral variables, benefits, and experiences); (3) the methodology used for the segmentation; and (4) the variables that emerge as influencing the assignment of the cultural tourist into the different segments. Whereas many studies emphasize the importance of sociodemo graphic variables in this respect, others find no or just a small influence of these variables (Prentice and Anderson 2007; Prentice, Witt, and Hamer 1998). They stress the importance of the trip profile patterns, the psychographics variables such as perceptions and motivations or other external variables such as meteorological conditions. It seems that each destination results in a specific typology of cultural tourists based on the characteristics of the site, the methodologies used, the data collection methods, the phrasing of the questions, and so forth. As previously mentioned, the contribution of the present study emerges mainly from the accurate unbiased database used for the segmentation. Nevertheless, a slightly different methodological approach to characterize the segments is taken here; the differences between the segments in terms of their sociodemographics, visitation patterns, and psychographics variables are tested applying discrete-choice modeling. The discrete-choice modeling method is applied by several authors in cultural tourism studies but for purposes other than segmentation. It is used mainly for forecasting or evaluating demand, based on visitors’ stated preferences (Louviere and Hensher 1983; Costa and Manente 1995; Mazzanti 2003). Maddison and Foster (2003) applied discrete-choice modeling to evaluate congestion costs in the British Museum. Boxall, Englin, and Adamowicz (2003) combined both stated and revealed preferences approaches via discrete-choice modeling to examine the value of unique aboriginal cultural resources. Apostolakis and Jaffry (2005) employed systematic, heterogeneous, discrete-choice modeling to evaluate consumers’ stated preferences for two Greek heritage attractions with the aim of learning about preferences of visitors belonging to

different segments. In this sense, the latter study presents a mirror image of the current study; while Apostolakis and Jaffry segmented the tourists according to their sociodemographics and other patterns and then characterized the segments according to the tourists’ stated preferences, we segment the visitors according to their revealed preferences and then test for sociodemographics and visitation patterns differences between those segments.

The Old City of Acre Acre is located at the northern end of Haifa Bay in Israel. From ancient times, Acre was regarded as the key to the Levant because of its natural harbor and strategic coastal location. Acre has a history of about 4,000 years and therefore is one of the oldest continuously inhabited cities in the world. Historically, the city was captured and held by the Greeks and then the Romans. The Arabs captured the city in 638, and held it until the Crusaders conquered Acre in 1104. Toward the end of the 18th century, it was revived under the rule of the Ottoman sheikh Dhaher El-Omar. Acre at the present time is considered a relatively small city with 52,000 Jewish, Muslim, and Christian residents. Although generally Acre’s different groups of residents coexist peacefully, there have been a few violent incidents over the years requiring police involvement and gaining the media’s attention. Because of this, it is necessary to consider the security issue when discussing tourism to Acre. In 2001, Acre, along with 30 other sites around the world, was declared a UNESCO World Heritage Site. The most important characteristic earning Acre this international recognition was the preservation of many historical layers dating back to the Crusaders’ era in such a small geographic area. The old city of Acre’s most popular attractions are the visitors center and Knights Underground Halls (see Figure 1, polygon A), the oriental market (see Figure 1, polygon E), the Templars’ Tunnel (see Figure 1, polygon Z), and the Mobility Corridor—a commercial area with restaurants and souvenir shops (see Figure 1, polygon B).

Segmentation Method The pattern of taking multiple-sites/-attractions tours is well documented in the literature. Among the reasons for visiting a destination with multiple attractions are the desire to seek variety, the different benefits each member of the tour group seeks, and a reduction of the uncertainty



A


A

and level of risk associated with taking a trip. Satisfaction with a particular attribute may be cumulative in the visitor’s utility function, and so a common attribute at a second attraction may still have a positive marginal utility. Also, seeking benefits from a diversity of attractions might result from decreasing marginal utility (McAlister and Pressemier 1981). Acre’s Old City indeed offers the potential visitor multiple attractions at a single destination. The decision-making process of the visitors is modeled using discrete-choice and censored data models. Following Loomis (1995) and Jeng and Fesenmaier (2002), it is assumed that visitors to the Old City of Acre make three levels of decisions. These decisions, the attributes of the attractions, and the attributes of the visitors interacting with them are the vehicles used for the segmentation. At the first level, the visitors decide whether to conduct a single-attraction tour and visit the main attraction only, that is, the visitors center, the Knights Underground Halls, and the Turkish Hamam (see Figure 1, polygon A), or a multiattraction tour that includes other parts of the city. This decision is the a priori criterion for the first segmentation. At the second level, visitors who decide to continue must choose which of the three main attractions to visit (see Figure 1, polygons E, Z, and B). These decisions determine the visitor’s affiliation to the

second a priori segments. The decisions at this stage are not independent: the decisions are made simultaneously, and factors affecting the decision to visit one type of attraction affect the decision to visit other attractions. Thus, the model we chose to use is the multivariate probit model, which allows for interdependence between the decisions. A cluster analysis would not allow this and would group the visitors into distinct groups not accounting for possible connections between the decisions. At the third level, after choosing an attraction, the tourists have to decide how much time to spend at that point. Here too, the decisions are not independent, assuming the visitors have a time constraint. The analysis of the decisions at the first two levels is based on the family of discrete-choice models (BenAkiva and Lerman 1985; Train 2003). The use of these models enables us to understand the behavioral process that led to the visitors’ choice of attractions. Some of the factors that determine the choice include the visitors’ own characteristics. Identifying which characteristic of the visitors affected the choice of attraction facilitates the affiliation of the visitors to the segments according to their attributes. Accordingly, visitors choose to visit the attraction j from a given set of attractions J. The utility the visitor n obtains from attraction j is Unj. This utility can be decomposed into Vnj—the deterministic part that is observed by the researcher—and εnj—the stochastic unobserved part. Thus, Unj = Vnj + εnj is a random utility function, and its distribution depends on the distribution of εnj. Vnj can include attributes of the visitor (Xn) and of the attraction (Zj) visited: Vnj = βXn + γZj , where β and γ are vectors of parameters. A visitor will choose to visit the ith attraction if the utility from attraction i is higher than the rest of the attractions: Uni > Unj j ≠ i. The probability that visitor n will choose attraction i is then Pni = Pr(Uni > Unj j ≠ i) = Pr (εnj – εni < Vni – Vnj j ≠ i). In the case of multiple attractions, the choice of attractions can be looked on as a repeated choice situation where the visitor has to choose repeatedly which attraction to visit. The coefficients in V can be estimated using different methods depending on the distribution of εs and the correlation between them (see Ben-Akiva and Lerman 1985; Train 2003 for further details). The estimation process of these choice probabilities results also in the correlation coefficients between the error terms. The latter can provide us with a valuable indicator of the nature of the relations between the different attractions on the site. Specifically, for complementary attractions, we expect the correlation coefficient to be significantly positive while, for substitutes, we expect it to be significantly negative. A

Figure 1 The Division of the Old City of Acre into 26 Polygons


Espelt and Benito (2006) found that the duration of visits to the different attractions in the city of Girona was an important element to distinguish between segments. Their observation that despite the heterogeneity of cultural tourist segmentation studies, there appears to be a recurring pattern of visitors with deeper interests in culture who consume the site at a higher level (spend more time, visit more attractions) was the impetus to further segment the visitors based on the duration of stay. Accordingly, we assumed a third level of decision wherein the visitors decide how much time to spend at each attraction. Since they have a total time constraint, these decisions are also not independent. If a visitor spends a long time at one attraction, he or she will naturally spend less at the others. Visitors maximize their utility from time on-attraction, subject to the time constraint allocated for the entire visit: Max uðtE , tZ , tB Þ − λðtE + tZ + tB − TÞ, where wheretE , ttEZ ,, ttBZ, tB , are ontE , tZ , tB attraction times for attractions E, Z, and B, respectively; T is the exogenous predetermined total time allocated for the visit; and l is the shadow price of the time constraint. Solving the problem yields the following demand functions of time on-attraction i: ti = f (tj,T,s) j ≠ i where i = E, Z, B, and s, the visitors’ attributes. The time spent at each attraction depends on the total time constraint (T, the time the visitor planned to spend at the entire site); the time spent at the other attractions, tj; and on the interaction between the visitors’ attributes and the realization of the attraction’s quality. We expect the more time the visitor planned to spend at the entire site, the longer he or she tends to actually stay at a chosen attraction at the site. However, the impact of time spent at other attractions can negatively or positively affect the on-attraction time, depending on the nature of the attractions. If the attractions are perceived as complementary, one should expect to see a positive effect; that is, the more time visitors spend at an attraction (tj), the more time they will spend at a complementary attraction (ti). The opposite effect is expected for attractions that are perceived as substitutes for each other. A

Data The database used for this study originated from a survey of a sample of visitors to the Old City of Acre. The survey combines GPS tracking technology to track down the visitors’ spatial and temporal behavior within an entire visit and the use of traditional questionnaires that collected other information regarding visitors’ socioeconomic and psychographics characteristics and the nature of their tours. The sample consists of 88

observations (including both individuals and small tourist groups) that were sampled on entering the site—implying a user-group sample. The GPS location kits were distributed only to individual visitors to Acre and not organized groups of visitors. Each such visiting group (e.g., an individual visitor, a couple, a family, etc.) received only one GPS device, and the key informant who filled out the questionnaire was always an adult person (in a case of a family with children). The Old City of Acre does not offer any tourist accommodation facilities besides a small youth hostel. Because of this and its relatively small size (no more than 125 acres), most visitors spend only a few hours touring the site. The purpose of the data collection process was to track the patterns of these tours as taken by a sample of visitors. Acre’s disadvantage as a destination with almost no tourist accommodations is an advantage when it comes to collecting data on the spatial behavior of visitors since most of the visitors come for only a few hours. Furthermore, the Old City has a main entry and exit point. To obtain a sample group, a stand was positioned alongside the Old City’s visitors center information booth. The center itself is located at the entrance to the underground Crusaders’ City, which, as the city’s most famous attraction, is usually the first site visitors choose to visit. Of the tourists, the few who had already spent some time in the Old City on the same day were rejected as viable participants since their tracks would not represent their entire visit. The remainder of the visitors were asked to do two things: carry the location kit—consisting of a GPS receiver and a pocket PC—with them throughout their entire tour and fill out a questionnaire. The GPS receiver transmitted, via Bluetooth technology, the visitor’s position to the pocket PC, which then logged the location’s coordinates. The GPS receivers obtained the location with an accuracy of a few meters. The questioners collected information regarding the visitors’ sociodemographic characteristics, the nature of their specific tours (time allocated for the visit to Acre, whether it was their first visit, the use of a city guide or a map, etc.), and finally, their interest levels regarding the diversity of cultural interests Acre has to offer. In addition, detailed land-use Geographic Information System maps were created, containing information on all possible attractions, shops, restaurants, stalls, and so on in the city. When combined with the highly accurate GPS receivers, these Geographic Information System layers made it possible to deduce with a high degree of certainty what the visitors’ activities were in each place they stopped for any significant time (for a more detailed overview about the methodology, see Shoval and Isaacson 2007a).



Table 1 A Description of the Main Touristic Attractions in the Old City of Acre Polygon A B C E U G J K N R Z

Description

Size in Square Meters

Visitors center, Knights Halls, and the Turkish Bath 18,173 Mobility Corridor, but also a concentration of restaurants and souvenir shops 4,425 The Light House—basically a panorama toward the Sea and Haifa 1,180 The oriental market (Souk, Bazaar) 5,577 Another panorama toward the Sea 2,159 Khan El Frange—courtyard: visitor attraction, not of high importance 3,849 The Ahmed el-Jazzar Mosque 3,141 Commercial area with many restaurants 6,210 Marina, fishing harbor 6,924 Khan al-Umdan—visitor attraction, but also a place by which people pass 3,146 Templars’ Tunnel 1,870

Data were collected on 19 nonconsecutive days during June, July, and August 2004. A total of 246 tracks were obtained, of which 107 tracks were discarded for reasons such as technical problems and uncompleted questionnaires. Finally, 88 questionnaires were found eligible for the specific analysis purpose described in this article. Acre’s Old City was divided into 26 polygons (measuring on average 2.2 acres) as depicted in Figure 1, with each polygon representing a location referring to a single activity. We believe that this division into polygons reflects the main compounds in the Old City of Acre. The visitors’ GPS tracks were coded so that once every minute, the system associated the letter assigned to the location in which the study participants were located at the time. Among the 26 polygons, 11 polygons qualified as tourist attractions or activities, so the analysis is focused on these polygons as described in Table 1. Of the 88 sampled tracks, 35 (40%) were restricted to polygon A (the visitors center and the underground Crusaders’ City). The other 53 tracks were engaged in one or more of the other attractions on the site (with the majority visiting more than one attraction). Out of the 10 remaining polygons (other than A), the following 3 polygons emerged as the most dominant attractions: Z—the Templars’ Tunnel, E—the Bazaar/Souk, and B—the Mobility Corridor and a concentration of restaurants and souvenir shops. All records include at least one of these three. In particular, 30 (34%) participants visited polygon B, 38 (43%) visited polygon Z, and 35 (39%) visited polygon E (by comparison, 2 visits were taken to polygon G, 6 to C, 4 to J, 6 to K, 10 to N, 9 to R, and only 1 to U). Accounting for the small number of observations at each of the 7 latter polygons, the focus was narrowed to the 3 dominant tourist attractions: Z, E, and B. Analyzing the temporal element of the tours, it was found that the duration of the visits ranged from 1 to 4 hours. The sample’s average total time in Acre’s Old City

Type Historic Commercial Landscape Commercial Landscape Historic Religious and historic Commercial Landscape Historic Historic

(including time spent in and between attractions) was 106 minutes, with 54 minutes’ standard deviation. When analyzing the time spent at the tourist attractions only, the average time declines to 78 minutes, with a standard deviation of 35 minutes. This accurate database allows the comparison of actual aggregated time spent with the planned time reported by the visitors in the questionnaires prior to embarking on their tours. It is found that on average the planned time was more than double that of the actual time; that is, the visitors did not exploit their entire planned time for the visit. The average total planned time exhibits high variation (similar to the variation of the actual time); while the sample’s average is 243 minutes (4 hours), the coefficient of variation is 50%. The shortest trip was planned for an hour, while the longest trip was planned to last for 8 hours (a whole-day trip). Examining the distribution of the ratio between the actual aggregate time spent in Acre and the planned time, we find that 87% of the sampled visitors overestimated the duration of their visits and stayed for less time than planned. Moreover, 64% of the visitors utilized less than 54% of the time they had allocated for the visit. Most visits to Acre’s Old City were taken by small groups rather than by single visitors. The average group consisted of 3.1 visitors, with a standard deviation of 2.1. Table 2 presents the descriptive statistics of the different variables that were used for the econometric analyses. These variables describe the sociodemographic and psychographic characteristics of the visitors as well as their visitation patterns, which emerge from the questionnaires. The average visitor to the Old City of Acre is a secular Jew, arrived from Israel’s southern areas in mixed-gender groups of three persons, does not have young children, and has a BA degree. The last variables in Table 2 describe motivations as reported by the respondents. These variables usually have a strong explanatory power



Table 2 Descriptive Statistics of Variables Employed in the Empirical Analysis Variable South Israela Abroada Secular Jewa Small kidsa No kids 40-59a Educationb Planned time Group size Weekenda Visita Knighta Vacationa Baha’i interestc Jewish interestc Historic interestc Ethnic interestc Pre fearc A_plusa Z_visita E_visita B_visita Z_stan E_stan B_stan

Description

Mean

= 1 if respondent is resident of Israel’s South area = 1 if respondent is tourist from abroad = 1 if respondent is nonreligious Jew = 1 if respondent is accompanied by small children = 1 if respondent’s group’s age is between 40 and 59 Education level of visitors Total time allocated to the visit in minutes Size of group the tourist was with = 1 if visit occurred during weekend days = 1 if the respondents visited Acre in the past = 1 if respondents visited the Knights Hall in the past = 1 if visiting Acre is part of a vacation Interest level in Baha’I religion Interest level in Jewish religion Interest level the historic layers of the city Interest level in Arab culture and Muslim religion Hesitation/fear levels before the visit = 1 if respondent performed multiattraction tour = 1 if respondent visited attraction Z = 1 if the respondent visited attraction E = 1 if the respondent visited attraction B Time per square kilometer in polygon Z Time per square kilometer in polygon E Time per square kilometer in polygon B

0.51 0.36 0.56 0.19 0.27 3.20 242.72 3.20 0.50 0.59 0.27 0.56 2.78 3.73 3.46 2.87 1.30 0.60 0.43 0.39 0.34 1.86 1.69 1.54

Standard Deviation

1.08 112.02 2.10

1.19 0.97 0.83 1.08 0.76

1.45 2.16 2.02

a. Indicates a dummy variable. b. On a 1 to 5 scale where 1 = primary school, 2 = high school, 3 = BA, 4 = MA, and 5 = PhD. c. Categorical ordinal variable on a 1 to 5 scale where 1 = not at all, 2 = slightly, 3 = moderate, 4 = to a big extent, and 5 = to a great extent.

for tourist choice behavior (Dalen 1989; Gonzales and Bello 2002). Visitors to Acre have reported on average levels of the different interests’ categories, with the lowest level for the Baha’i religion and the highest level for the Jewish religion. This reflects the fact that 80% of the visitors to Acre are Jews, either religious or secular. The variable labeled “Pre fear” in Table 2 expresses fear perceptions associated with visiting Acre. Measuring the effect of this factor is essential because, as described above, over the years there have been a few wellpublicized violent conflicts in the city, stemming from its diverse ethnic makeup. The sample’s participants were asked to refer to their fear perceptions prior to and after visiting the city. The sample average level of pre fear is 1.3, which is, as expected from a group that decided to visit, not very high.

Results The first-stage, a priori segmentation distinguishes between visitors who restricted themselves to polygon A and those who performed a multiattraction visit. Aiming

at identifying the difference between the two segments at the first-level decision, we applied a discrete-choice binary model. The binary dependent variable, A_plus, received the value 1 when the visitors decided to engage in a multiattraction tour and 0 when they decided to visit polygon A only. Comparing the relative performance of the binary logistic and normal probability (probit) models, we found that the probit model performs better; the standard errors of the estimated coefficients tend to be smaller than those of the logistic model. Also, the probit model results in higher rates of prediction success than the logistic. Therefore, the probit model was chosen. Having run several models’ specifications (with various combinations of regressors), the selected model, whose results are presented in Table 3, is the one that performs best and results in the highest predictive ability for the dependent variable. The coefficients from the probit model are difficult to interpret because they measure the change in the latent unobservable dependent variable associated with a change in one of the explanatory variables. For that reason, it is customary to calculate the marginal effects,



Table 3 Results of Probit Equation Standard Marginal Variable Coefficient Error Effects Elasticity Index function for probability Constant –0.933 1.399 –0.346 South Israel 1.243* 0.391 0.439* Pre fear –0.559* 0.259 –0.207* Secular Jew –0.372 0.366 –0.136 Historic interest 0.215 0.209 0.080 Jewish interest –0.118 0.186 –0.044 Group size 0.453* 0.191 0.168* Education –0.001 0.167 –0.002 Vacation –0.347 0.345 –0.127 Weekend 0.152 0.325 0.056 Small kids –1.030** 0.593 –0.393* Planned time 0.001 0.001 0.001

– 0.346 –0.415 –0.117 0.426 –0.251 0.731 –0.008 –0.107 0.043 –0.115 0.202

Note: Correct prediction = actual 1s and 0s correctly predicted 77.4%. *Significant at 5%. **Significant at 10%.

that is, the change in the predicted probability associated with a change in the explanatory variables (e.g., Greene 2000, p. 667). Hence, Table 3 presents both the regression coefficients and their derived, calculated, marginal effects. The estimated coefficients in Table 3 identify the differences between the two segments. The first segment of visitors, who tend to explore the city and engage in multiple-attractions tours, is characterized by residents from Israel’s southern areas coming in relatively big groups not accompanied by small children. These visitors cover quite a distance to reach Acre and thus are likely to explore the city further. They are also relatively mobile, without small children. An important variable that characterizes this segment is that they do not perceive Acre as insecure and have a lower fear perception than the visitors in the other segment. The marginal effect of the pre fear variable is –0.21; that is, an increase of 1 unit in the pre fear variable results in a decrease of 21% in the probability of leaving the visitors center. Visitors in the first group are segmented further according to their choice of attractions. As the preliminary statistics analysis revealed (illustrated in the Data section), out of a choice set of 10 attractions inside the city, 3 are very frequently visited while the others are only rarely visited. Due to the small number of visits to the latter attractions, we focused on the decisions to visit the 3 dominant attractions—Z, E, and B—and segmented the visitors based on their decisions to visit each one of these attractions in accordance with Dolnicar’s (2004a)

concept 3. The reasoning behind this segmentation derives from the fact that each one of these 3 attractions represents a different facet of the cultural product: Z as a pure historical attraction, E as an ethnic/folkloristic attraction, and B as a commercial area that also provides the opportunity to shop, dine, and so forth. Accounting for biases resulting from possible correlations between the second-stage decisions and the first-stage decision, we ran several models with selection and found that no significant bias exists: the correlation disturbance, rho, was never significant. Hence, the second stage’s decisions could be modeled independently from the first stage’s ones. Since the 3 attractions embodied different facets of the site, we modeled the decisions to visit each attraction separately, applying the multivariate probit model for that purpose. There are three binary dependent variables—Z_visit, E_visit, and B_visit—which receive the value 1 if the visitor visited attractions Z, E, and B, respectively. The multivariate probit model for consumer choice is theoretically appealing as it is free from the “independence of irrelevant alternatives” property of the multinomial logit model. However, difficulties in estimations for a large number of alternatives in the choice set have restricted its widespread use. In our case, of 3 alternatives, this model can be applied and its benefits exploited. The multivariate probit also accounts for the fact that the same person can visit a few attractions, and thus the decisions are correlated. All three equations are estimated together, allowing the disturbances in the equations to be freely correlated (Train 2003). Inasmuch as the number of observations at the second stage decreases, we checked the validity of our results by estimating the model using a simple probit model (three independent models, one for each of the 3 attractions) and a simple logit model in addition to the multivariate probit. The estimated coefficients in all three model types were similar. The selected model (see Table 4), which was estimated using the maximum likelihood estimation procedure, performs the best in terms of the explanatory variables. It should be noted that the observations at this stage are more homogeneous than at the first stage. Additional variables did not add much to the explanatory value of the estimation (the likelihood function value hardly changed), whereas the significance of the existing variables decreased. Thus, fewer explanatory variables were included in the model. From the correlation coefficients of the disturbance terms at the bottom of the table, it can be seen that there is a high positive correlation between the unexplained parts of the decisions to visit the Templars’ Tunnel (Z) and the oriental market (E) and a negative correlation



Table 4 Multivariate Probit Results for Choosing Z, E, and B Variable Z—Templars’ Tunnel Constant Planned time Ethnic interest Historic interest Visit E—Bazaar Constant Planned time Ethnic interest Historic interest Visit B—Commercial area Constant Planned time Ethnic interest Historic interest Visit R(Z, E) R(Z, B) R(E, B)

Coefficient 2.233** 0.004* 0.255 0.134 0.956*

Standard Error 1.320 0.002 0.202 0.343 0.509

–1.720 0.000 0.461* 0.134 0.386

1.682 0.002 0.242 0.370 0.443

2.455 0.001 –0.301 –0.292 –1.094** 0.696* –0.406 –0.575*

1.941 0.001 0.209 0.390 0.631 0.225 0.450 0.301

*Significant at 5%. **Significant at 10%.

between the decisions to visit to the oriental market (E) and the commercial area with souvenir shops and restaurants (B). This means that for a reason that is not controlled for by the explanatory variables, visitors who visit E will tend to visit Z, while those visiting B will probably not visit E. These results can be interpreted on the ground that Z and E are perceived as complementary activities (Z as a pure historical site and E as an ethnic/ folkloristic site) while E and B are perceived as substitute activities (B as a commercial area and E as an area that also provides the opportunity to shop, dine, etc.). Visitors who planned to stay a longer time at Acre and who had visited it in the past were more likely to visit the Templars’ Tunnel. The Templars’ Tunnel was opened to visitors only in 2002, and it is possible that people who visited Acre in the past came specifically to see the Templars’ Tunnel. Moreover, the Templars’ Tunnel is located the farthest from the visitors center, and it takes a while to get there. This explains why people who planned a longer visit tend to visit there. Visitors who are interested in an Arab ethnic experience are likely to visit the oriental market, which provides exactly that. Visitors to the commercial area (B) are usually first-time visitors to Acre. The analysis for the second level of decision identifies the difference between the subsegments that emerge from the group that decided to visit the whole of Acre’s

Old City at the first level of decision. The first subsegment includes visitors who visit the oriental market and the second one those who tend to visit the Templars’ Tunnel. These two subsegments are not mutually exclusive and actually can be combined since people who visit one tend to visit the other (as the correlation between the error terms reveals). The other subsegment includes visitors who tend to visit the commercial area (B) solo. The third level of decision regards the on-attraction time. This decision provides the basis for further segmenting the visitors according to their experiential consumption of the attraction. Choosing to visit the attraction is one thing, but staying longer at a given attraction indicates a deeper level of experience. We therefore a priori segmented the visitors according to three subsegments: those staying longer at Z, those staying longer at E, and those staying longer at B. We expected that the total time constraint and the visitors’ attributes, in the context of the realization of the attraction’s quality, would affect their affiliation to the different subsegments. Another important factor influencing the on-attraction time is how long they stay at the other attractions. Accounting for the time constraint, visitors will spend less time in substitute attractions and more time in complementary attractions. Due to the simultaneity of the dependent variable and the truncated nature of the data (not all visitors visited all the attractions), the best model is simultaneous Tobit (Greene 2000). The Tobit belongs to the family of censored models. These are applicable when a realization of the dependent variable is observed only when it crosses some critical threshold. Since not all visitors visited all attractions, a censored model is required to estimate the demand for time on a given attraction. Controlling for attraction size and accounting for initial time constraint, group size, and other variables, Table 5 presents simultaneous Tobit regressions for the time spent at each of the three most visited attractions in Acre’s Old City: E, B, and Z. The dependent variable in each regression is the on-site time divided by the total size of the attraction in square kilometers (E_stan, B_ stan, and Z_stan). The table presents both the regression coefficients and the marginal effects computed at the means of the explanatory variables. The results suggest that the longer the visitors stay in Z, the longer they stay in E, whereas the opposite is true for B. We can also see that visitors who show interest in Arab culture and Islamic religion will stay longer in the oriental market (an increase of a unit in interest in Arab culture and Islamic religion increases on-attraction time in polygon E by 0.66 minute per square kilometer, that is, by about 4 minutes), and visitors who have an interest in historic periods of the city will stay longer in the



Table 5 Estimation of Tobit Simultaneous Regression System Variable

Coefficient

Standard Error

Marginal Effects

Oriental market (Souk, Bazaar) Constant –1.855 2.180 –1.212 Knight 0.078 0.757 0.051 Pre fear –0.899 0.656 –0.582 Ethnic interest 1.013* 0.421 0.661* Baha’i interest –0.339 0.393 –0.221 Historic interest –0.449 0.384 –0.292 Group size –0.059 0.151 –0.041 Education 0.018 0.021 0.012 Secular Jew 1.499* 0.812 0.981* No kids 40-59 0.393 0.836 0.261 Planned time 0.001 0.003 0.000 Z_stan 0.899* 0.215 0.591* B_stan –0.209** 0.119 –0.141** Disturbance standard deviation: Sigma 2.164 0.270 ANOVA-based fit measure = 0.211 DECOMP-based fit measurea = 0.303 Z—Templars’ Tunnel Constant –2.461 1.620 –2.041 Knight 1.342* 0.614 1.110* Pre fear 0.912** 0.499 0.762** Ethnic interest 0.008 0.282 0.007 Historic interest 0.542** 0.302 0.451** Group size 0.110 0.125 0.089 Education –0.007 0.017 –0.006 Abroad 0.006 0.772 0.006 Secular Jew –0.909 0.627 –0.754 No kids 40-59 0.292 0.643 0.242 Planned time 0.002 0.002 0.001 E_stan 0.517* 0.134 0.431* B_stan –0.018 0.093 –0.009 Disturbance standard deviation: Sigma 1.751 0.216 ANOVA-based fit measure = 0.330 DECOMP-based fit measurea = 0.341 B—Mobility corridor and commercial area Constant 4.199 4.725 2.119 Knight 1.316 1.623 0.662 Pre fear –2.002 1.781 –1.011 Ethnic interest –0.207 0.805 –0.102 Baha’i interest –0.082 0.698 –0.042 Historic interest –0.285 0.859 –0.143 Group size –0.382 0.479 –0.192 Education –0.026 0.043 –0.014 Abroad 2.960** 1.860 1.490** Secular Jew 2.800** 1.591 1.410** No kids 40-59 2.382 1.606 1.202 Planned time 0.001 0.005 0.001 Z_stan –0.363 0.532 –0.181 E_stan –0.477 0.395 –0.241 Disturbance standard deviation: Sigma 4.074 0.573 ANOVA-based fit measure = 0.221 DECOMP-based fit measurea = 0.280

Standard Error 1.391 0.491 0.421 0.271 0.262 0.251 0.092 0.013 0.532 0.552 0.002 0.142 0.079

1.321 0.501 0.414 0.231 0.253 0.102 0.013 0.642 0.521 0.531 0.002 0.112 0.081

2.371 0.832 0.884 0.414 0.352 0.429 0.244 0.022 0.922 0.809 0.814 0.001 0.272 0.201

Note: DECOMP = decomposition a. In the case of Tobit models, this fit measure, as well as the ANOVA, is best suited to be used as a substitute for the Ordinary Least Squares R2. The R2 DECOMP takes the variance of the conditional mean function around the overall mean of the data in the numerator (Green, 2002). *Significant at 5%. **Significant at 10%.



Templars’ Tunnel. These results indicate that the more visitors are interested in the nature of the attraction, the more time they will spend there. Nonreligious Jews tend to spend more time in the commercial attractions (E and B). This can be explained by the fact that religious Jews have dietary restrictions and thus cannot eat in many restaurants that exist there. Foreign tourists are also more likely to spend more time in the commercial area, B, probably because they can find souvenir shops there. The third-level decision enables fine-tuning of the segments and identifies the third layer of segments. The subsegments from the second level are regrouped now depending on the time allocation. It seems that visitors spending a longer time in E and Z are those showing greater intellectual and cultural interest in what the attractions have to offer. Whereas visitors spending more time in the commercial areas, E and B, are nonreligious Jews, those who spend more time in B are foreigners who are engaged in souvenir shopping more than the Israeli tourists.

Summary And Conclusions This study uses questionnaires and GPS tracking of visitors to the Old City of Acre to classify them into different segments. Until now, researchers had to rely on the visitors’ statements, which are not always accurate, or to actually follow the tourists, which can be logistically difficult, expensive, and not as accurate as GPS tracking. These very detailed data of the visitors’ behavior at the site juxtaposed with a questionnaire can serve as an important tool for researchers and practitioners to uniquely segment each site. To our knowledge, this is the first time GPS data have been applied to a tourist segmentation study. The segmentation concept follows Dolnicar’s (2004a) concept 3, wherein the basis for segmenting is the visitors’ decision making throughout the visit. The validity of the a priori segments is tested, through discrete-choice analysis methods and a system of censored regressions, against visitors’ sociodemographics and psychographics variables. The methods of discrete-choice and censored regressions enable us to understand which attribute of the visitors affect their decision to visit different attractions at the site and how long to stay there, respectively. The estimated coefficients identify the different segments. This analysis does not group the visitors into distinct groups but supplies a more flexible structure, which allows resegmenting the visitors at each decision level. This analysis can be used by site managers and the marketing authority to bundle certain attractions or adjust them to the needs of other groups that do not visit them.

In the case of Acre, we found that at the first level of decision, whether to visit the whole city or to stay at the visitors center only (polygon A), visitors who live at a distance from Acre, who are not accompanied by young children and thus are more mobile, and who do not perceive Acre as insecure tend to visit the whole city. This information can be used by tourism officials to draw visitors who have already arrived at Acre but have decided to stay at the visitors center only. One way to draw these visitors inside the city is to promote and emphasize the security of the city. Another way is to invest in attractions for children or alternatively to emphasize existing child-oriented attractions to draw families with children. It was also found that visitors who decided to expand their visit to the whole city and visited attraction Z (Templars’ Tunnel) also tended to visit attraction E (the oriental market) and shared the same heritage interests. These results were assessed also at the third level of decision for how long to stay at each attraction. Here also, the level of intellectual interest in the attraction positively affects the duration of the visit. This information points to marketing E and Z as a bundle because of the complementary nature of the two attractions. The recent rapid development and availability of small, nonexpensive, and reliable tracking devices has led to a growing volume of spatial behavior research. These tracking devices enable the easy tracking of participants in time and space during long periods of time and provide researchers with an extraordinarily high degree of temporal and spatial resolution. These new types of databases can serve to advance current lines of inquiry in tourism studies and, more importantly, open up a great many new ones that previously were unfeasible. They can be used, for example, to better understand retail and consumption geographies of tourists or to measure and assess the vitality of tourist-historic cities. They facilitate the analysis of tourists’ activities that involve mobility within a destination such as a theme park or in between destinations in a tourism region. A limitation of this study results from the relatively small number of observations used for the regressions. This is due to budgetary and technical reasons involved with the application of GPS data collection. These problems need to be overcome if this is to become a common data collection method for different purposes. Nevertheless, despite the small number of observations, the empirical analysis results in significant coefficients that enable the assignment of visitors to the different segments. Finally, the study is restricted to one heritage site; thus, it is difficult to make a broad generalization about cultural tourists’ segmentation between destinations.



That is, each site has its own unique attributes, and its tourist segments are most likely not transferable to other sites. Although it might be considered limiting, we think that the information provided by such a segmentation method presents an important tool for site managers and other tourism officials in optimizing the tourism experience for the visitors and revenues for the local tourism services suppliers. Although the segmentation itself is not transferable, the method of data collection and its analysis can be adopted in different sites.

Note 1. Not all the reviewed studies here are merely segmentation studies; nevertheless, they provide to some extent a classification of heritage tourists according to one or more criteria.

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Anat Tchetchik, PhD, is a lecturer in the department of hotel and tourism management at the Guilford Glazer School of Business and Management, Ben-Gurion University of the Negev. Her research interests are tourism economics, rural and regional economics, and empirical industrial organization. Aliza Fleischer, PhD, is a senior lecturer in the department of agricultural economics and management and head of the Hospitality, Food Resources and Tourism Program at the Hebrew University of Jerusalem. Her research interests involve tourism and natural resource economics. Noam Shoval, PhD, is a senior lecturer at the department of geography, the Hebrew University of Jerusalem. His research interests are tourism and culture and urban regeneration, models of hotel location, and the use of tracking technologies for analysis of tourists' time space activities.
