The Role of Heritage and Cultural Elements in Coastal Tourism Destination Preferences

Abstract

Despite the shifting demands of coastal tourists, little information exists on the consumer demand for cultural or heritage elements in coastal destinations.This study examines tourists' demand for regional character when vacationing on the South Carolina coast. Preferences for authentic trip elements such as the destination's local character and the local ownership of restaurants were measured using stated preference choice modeling. This method forces individuals to make choices between hypothetical trips based on tradeoffs of attributes such as number of activities, amount of locally owned restaurants, degree of local character, and trip cost. The results of a survey with South Carolina Coastal Tourists (N = 697) indicate significant preferences for local character and local restaurant ownership, suggesting that tourists likely have a considerable desire for regional character in their vacations. However this desire was lower than the desire for other attributes that did not involve cultural or heritage elements.

Keywords

cultural tourism coastal tourism tourism demand choice modeling

Introduction

Oceans, lakes, and rivers provide a number of distinct economic advantages to cities and towns located along the coast. Chief among these benefits are the transportation and fishery-based industries that can only be located on the coast (Cincin-Sain and Knecht 1998; Gallup, Sachs, and Mellinger 1999). Coastal settlements have heavily relied on these industries for decades, and these industries have greatly influenced coastal development (Kildow and McIlgorm 2010). Recently, however, competition from the tourism industry, coastal gentrification, and subsequent impacts on marine ecosystems have resulted in a notable decrease in traditional commercial usage of the coast and the slow decline of many traditional coastal industries (Barkley, Henry, and Gantt 2004; Smythe 2010). These obstacles are especially pronounced in popular coastal tourism destinations where there is intense competition for limited waterfront real estate. It is increasingly apparent that the survival of traditional coastal businesses has been weakened by a complex interaction of variables including the decline in availability of and access to marine resources, competition for waterfront space, increased global competition, limited marketing capacity, and infrastructure costs (Barkley, Henry, and Gantt 2004; Johnson and Orbach 1990).

Faced with the potential loss of these important industries, community economic development initiatives and policies continue to focus on integration of traditional marine resource–dependent industries with other tourism attractions and services (Cincin-Sain and Knecht 1998; Sharbaugh 2011). Access to traditional coastal features including an active fishing industry (i.e., fishermen, vessels, and processing facilities), commercial wharfs, local crafts made from coastal resources, fresh-caught seafood, and seafood cuisine can create a unique experience in coastal regions and contribute to a vibrant heritage/culture. These features in turn benefit tourism and the local economy by attracting a wider range of tourists seeking unique experiences. Consequently, the survival of these water-dependent businesses is important to differentiating the tourism destination as these industries frequently add unique cultural heritage values (Walker and Arnn 1998).

These heritage and cultural elements are becoming more important as traditional coastal destinations that focus on sun, sand, and surf (3S) may be slowly becoming obsolete. Tourists are increasingly demanding more unique and authentic products such as heritage tourism (Agarwal 2002; Sedmak and Mihalic 2008). One of the shortcomings of conventional coastal destination development is the strong homogenizing effect on the area’s culture and appearance that often leaves the destination devoid of any unique and meaningful character (Gale 2005). This homogenization may cause problems in the future for coastal destinations as new, postmodern tourists demand unique and authentic experiences and activities in their vacation destinations (Poon 1993). Agarwal (2002) indicates that this new type of demand is leading to a restructuring of the coastal tourism industry.

While this restructuring may entail difficulties for coastal destinations as they are forced to meet changes in demand, it may also present new economic opportunities. Strategies focused on re-creating and strengthening the linkages between tourism and traditional coastal industries have potential to support traditional businesses while retaining the more authentic experiences and products for attracting discriminating tourists to the destination. However, there is a lack of understanding of consumer preference for coastal destination attributes, particularly the relative importance of demand for the heritage elements of tourism that are tied to the actual presence of coastal-dependent industries. Therefore, this study intends to examine consumer preferences for coastal tourism destinations, paying particular attention to consumer interest in the heritage elements of coastal destinations.

Consumer Demand for Coastal Tourism

Coastal Tourism Planning and Management

Sustaining the long-term vitality and market competiveness of a coastal tourism destination depends upon evaluation of current products, development of new products, and continued evolution of complementary facilities that serve the varying needs of tourists. New, properly planned products and services that are unique to the destination can increase the potential for attracting and retaining new tourists, and help achieve economic benefits at the local level. Despite the necessity of understanding consumer preferences for products and services, there is a lack of information on consumer preferences for coastal tourism, which may have a long-run detrimental effect on the health of coastal destinations and communities (Hall 2001). Potential problems include substantial difficulties in developing appropriate policies and the coordination of the various elements of the tourism product (Hall 2001). Understanding consumer preferences is particularly relevant to informing decision making about integrated coastal management when developing sustainable tourism is a priority. Better understanding tourists’ preferences can lead to the development of more economically viable destinations and marketing and planning strategies.

The lack of understanding of consumer preferences for sustainable coastal tourism destinations is surprising given the popularity of coastal tourism. Coastal destinations have traditionally relied heavily on the 3S style of tourism. However, a shift away from 3S tourism may be occurring for reasons involving both supply and demand (Agarwal 2002; Sedmak and Mihalic 2008). On the supply side, tourism industries have faced increasing competition from new coastal destinations, particularly those in the developing world that may be able to provide more unique and exotic trips at competitive prices (Gormsen 1997). In addition, factors such as the increasing cost of real estate and local competition have cut into profit margins (Agarwal 2002; Warnken, Russell, and Faulkner 2003).

On the demand side, changes in tourists’ values and preferences have caused a portion of demand to shift away from mass tourism experiences. These tourists “reject inflexible and inauthentic products developed for mass markets and resist the globalizing and homogenizing effect of modernity” (Sedmak and Mihalic 2008, 1008). These changes create two challenges for 3S style coastal tourism. First, tourists are demanding experiences that are custom tailored to their needs and desires, which traditional coastal destinations have a difficult time providing (Gale 2005). Second, tourists are increasingly demanding experiences that are more unique and authentic, which is often limited in 3S style destinations because of the homogenizing effects 3S development has on the destination. These changes have led many researchers to believe that many traditional coastal destinations are stagnating (Priestley and Mundet 1998), and this stagnation in turn suggests that more emphasis be placed on heritage and cultural attractions in coastal areas (Sedmak and Mihalic 2008).

Heritage and Cultural Tourism in Coastal Environments

Heritage tourism has been defined from a supply (Palmer 1999; Yale 1991) and demand side (Dahles 1998; Poria, Butler, and Airey 2001; Richards 1996; Silberberg 1995). Supply-side definitions focus on historically and culturally significant attributes of the site itself. For instance, Yale (1991) defines heritage tourism as “tourism centered on what we have inherited, which can mean anything from historic buildings, to art works, to beautiful scenery” (p. 21). Demand-side definitions focus on the motivations and experiences of the tourists. For example, Silberberg (1995) defines heritage tourism as “visits by persons from outside the host community motivated wholly or in part by interest in historical, artistic, scientific or lifestyle/heritage offerings of a community, region, group or institution” (p. 361). From a practical perspective, heritage and cultural tourism has been suggested as a tool for regional economic development for a number of years (Advisory Council on Historic Preservation 2008; Alliance of National Heritage Areas 2005). The concept of heritage/cultural tourism has a variety of key potential benefits for regional development, namely, that heritage tourism has a growing segment of consumers, is capable of generating large economic impacts, often requires lower levels of investment, and has the potential to create high linkages with the local economy (Strauss and Lord 2001). These impacts have been measured in events, single attractions, and heritage trails (Bridaa, Meleddub, and Pulinac 2012; Bowitz and Ibenholt 2009; Herrero et al. 2006; Strauss and Lord 2001). Heritage tourists are even noted to spend more than other segments of tourists (Carey, Gountas, and Gilbert 1997; Kerstetter, Confer, and Graefe 2001; Nicolau 2011).

Coastal communities often have a latent supply of heritage products that are directly linked to coastal resources. These products include the traditional fishing industry commercial wharfs, local crafts made from coastal resources, fresh-caught seafood, and coastal cuisine that are unique to the region and culture. Despite these assets, heritage is not traditionally thought to be a major factor in most coastal destinations (Gale 2005). However, Sedmak and Mihalic (2008) found a demand for authenticity and heritage products among coastal tourists. In addition, previous studies with coastal tourists indicate an interest in authenticity in food and food-related experiences (Deale, Norman, and Jodice 2008). Coastal development planners have been increasingly working to integrate the traditional coastal industries with other waterfront-based attractions and services (e.g., restaurants, retail sales, charter fishing, marina facilities, and maritime museums) (Hall 2001). This strategy may involve creating a unique waterfront destination, which, when planned as a vital public space, may attract certain segments of tourists and enhance economic sustainability of the coastal-dependent businesses. These types of complementary organizational networks where complementary products of activities, accommodation, transport, and food coexist with support activities and infrastructure in a tourist destination can provide the foundation for building sustained competitive advantage (Porter 1998).

Despite enthusiasm from a number of scholars, researchers caution that the potential of heritage tourism has been overhyped. McKercher and Chan (2005) argue that the idea of heritage and cultural tourism has been exaggerated by faulty methodologies and that

the subsequent misrepresentation or misinterpretation of results influence tourism policy, marketing, and product development decisions in both the public and private sectors. . . . Confusing actions with motives leads to poor decision making, which explains why phantom demand continues to affect this sector. . . . Indeed, the apparent mass interest shown in [special interest] tourism rarely translates into real commercial opportunities. (p. 30)

They suggest that visiting culturally significant sites is not enough to deem a tourist a cultural/heritage tourist. Rather a holistic understanding of the tourists’ desires and demand is required before researchers can assign them to a particular niche. Similar research has indicated that cultural attractions may not be as popular as typically suggested (Poria, Reichel, and Cohen 2011), may not be as important to tourists as more traditional activities and attractions (Lacher and Harrill 2010), and that cultural motivations are typically held alongside other motivations (Boley, Nickerson, and Bosak 2011). Understanding the importance of heritage and cultural elements to tourists relative to other attributes of a destination is therefore important in understanding the importance of heritage tourism.

Study Contributions

Work on the specifics of consumer demand for heritage products that are dependent on marine resources is still in its infancy. Given the predicted increase in this demand type and the changes occurring in coastal communities, it is important to continually examine the specifics of demand for the coastal tourism. While following similar research questions and methodological approach to that of Sedmak and Mihalic (2008), this study adds to the literature by both assessing consumer preferences measured with tourists’ marginal willingness to pay (MWTP) for authentic attributes and by comparing the demand for authenticity to the demand for quality and quantity of other important destination attributes. This type of analysis will help provide insights into the relevance of heritage and cultural resources to coastal tourism. In addition, by surveying in both areas known for heritage tourism and areas with few heritage attractions, a more comprehensive understanding of how the specifics of consumer demand varies among tourists on different types of vacations will be achieved.

Methods

Study Area

Tourism is a major driver of the South Carolina coastal economy. The three coastal counties of Horry, Charleston, and Beaufort generated more than $5.7 billion in domestic travel expenditures in 2008; this represents 58.2% of South Carolina’s total tourism expenditures (U.S. Travel Association 2009). While most of the coast is known for its beaches and resorts, tourism in the Charleston area relies heavily on local heritage and cultural resources and is known for possessing the charm of the “old south.” While tourism in the region is growing (Tourism Development International Ltd. 2006), traditional industries are facing hardships. This is especially true of the fishing industry, which has declined dramatically because of competition with low-priced imports and rising fuel prices (Barkley, Henry, and Gantt 2004).

The South Carolina Coast serves as a useful study area because of the diverse nature of coastal tourism destinations. The city of Myrtle Beach dominates the northern portion of the coast and is known as a low cost, family-friendly destination that has numerous affordable attractions and amusement park–like activities. Charleston is located in the central region and is known for showcasing southern culture and history. Hilton Head and Beaufort are on the southern coast and are primarily an upscale resort destination with less development and more pristine coastal landscapes. These differences are reflected in the marketing and promotion of the different locations (SC Department of Parks, Recreation and Tourism 2012). One interest of this study is to evaluate how the demand for cultural and heritage elements differed across these vastly different destinations.

Survey Design and Implementation

The stated preference choice modeling (SPCM) method was employed to understand tourists’ preferences for coastal vacations. SPCM makes use of hypothetical scenarios to elicit public responses regarding the examination of the relative importance of trip attributes and the trade-offs that tourists are willing to make between/among these attributes (Bennett and Adamowicz 2001). SPCM is relevant for this study because it evaluates trade-offs that consumers must make when deciding between vacations; therefore, the relative importance of different vacation attributes can be evaluated. SPCM is an ideal tool for measuring these types of complex tradeoffs (Louviere 1988) and is popularly used in the applied marketing world because the results of the analysis are considered reliable approximations of real consumer behavior (Louviere, Hensher, and Swait 2000). Finally, SPCM is an ideal choice because it allows researchers to calculate MWTP values for nonmarket goods such as amount of heritage.

The first step of SPCM is to select important attributes and subsequent levels. A list of fifteen attributes and levels was initially created by collecting input from a number of researchers and extension agents with experience in coastal tourism research and/or management. The first focus group was conducted with South Carolina coastal tourists with eight participants. They were asked to generate their own list of attributes; this list was then combined with the researchers’ list. The focus group then worked to eliminate attributes deemed less important so that the finished survey would have a reasonable number of attributes. Finally, the group worked to create meaningful levels for the attributes and to make the language of the survey easily understandable to coastal tourists. The second focus group was conducted with the five owners of coastal businesses to determine if there were any important attributes left out and to reconfirm the validity and wording of the attributes. The second focus group generally agreed with the attributes and levels, and made some suggestions to improve the wording of the survey.

The importance of heritage and cultural aspects of coastal vacations were evaluated through two separate attributes: the ownership of the destinations’ restaurants and the amount of local character showcased in the destination. Local restaurant ownership not only increases the amount of local heritage represented in the dining experiences, but also typically has more connections to local food production and thus can quickly connect tourism to traditional fishing and agricultural businesses (Telfer and Wall 1996; Torres 2003). While local restaurant ownership may seem a trivial part of a coastal tourism vacation, food has been increasingly recognized as an important element in the travel experience (Cohen and Avieli 2004). Furthermore, a number of destination marketing organizations along the South Carolina coast have promoted the unique “lowcountry” style of food as a major part of their tourism advertisements (SC Department of Parks, Recreation and Tourism 2012).

The other heritage related attribute included in the survey was the amount of local character and personality that exists in the tourism activities. The term character was deliberately chosen to describe the cultural and heritage elements embedded in local activities as the focus groups participants understood the idea of character more than other terms such as “heritage” or “culture,” which were also considered. This attribute serves to indicate the importance of a lowcountry feel to South Carolina vacations. The presence of traditional industries such as fishing and agriculture may contribute to this feel as may locally produced crafts (Che, Veeck, and Veeck 2005; Daugstad 2008; Littrell, Anderson, and Brown 1993). A high demand for local character would indicate the need/potential to connect local industries to tourism to both stimulate the local economy and enrich the vacation experience.

In addition to these attributes involving cultural/heritage elements, focus group participants highlighted four other important, often more typically thought of, attributes they evaluate when considering coastal vacations. These attributes provided a more holistic evaluation of consumer preference for vacation destinations and allowed this study to compare the importance of the heritage-related attributes to the demand for these other attributes. These included (1) degree of destination development (Destination); (2) activities available at or near the destination chosen (Availability of activities); (3) quality of the food at the restaurants (Restaurant quality); and (4) total cost of a coastal trip (Trip cost). In sum, the six attributes include two relating to heritage (activities’ emphasis on regional character, restaurant ownership) and four relating to more traditional vacation elements (trip cost, availability of activities, restaurant quality, destination). Based on focus group input, three or four different levels were assigned to each attribute, and these attributes and levels were presented with succinct definitions (see Figure 1).

Figure 1.

The script for the SPCM section and an example of a paired choice set for a trip to a coastal destination.

After determining attributes and levels, the paired choice sets consisting of two trip options (“Trip A” and “Trip B”) and a neither trip (“I would not go on either trip”) option were created. To work with a reasonable number of paired choice sets, an efficient fractional factorial experiment design method with main effects only (i.e., resolution III designs) suggested by Kuhfeld and Tobias (2005) was used. The design under a D-efficiency criterion generated 36 paired choice sets. Because of a concern about dominant choice sets, we deliberately eliminated them in the design stage. These choice sets were then divided into six blocks of six paired choice sets that are uncorrelated using a randomized complete block design. This design method assumes that blocks as groups of experimental units should be as homogeneous as possible (SAS Institute Inc. 2004). This was considered an effective way to reduce the number of trip choice sets any one respondent faces. Thus, six different versions of the survey questionnaire were used, each containing six paired choice sets (see more of the designs from Kuhfeld and Tobias [2005]). Consequently, each respondent was asked to answer only six paired choice sets. An example of one paired choice set is provided in Figure 1. In addition to the choice sets, the survey included a number of questions regarding the respondent’s demographics and trip. The demographic characteristics of age, income, and gender were also used as independent variables in the SPCM analysis; information on the cost of the trip and the number of days of the trip is used in willingness-to-pay estimation.

The survey was conducted during fall 2008 and summer 2009. Tourists (operationalized as visitors that were not a resident of the destination counties) were intercepted in the three major coastal tourism destinations, Myrtle Beach, Charleston and Beaufort/Hilton Head, in a variety of venues (e.g., beaches, visitor centers, state parks, downtown areas). If they agreed to participate in the study, tourists were asked for their name and mailing address. Questionnaires were then mailed using a modified Dillman (2000) survey method. A pre-notice letter was not sent as the tourists had been approached on-site and informed of the survey. The first mailing occurred one week after the intercepts and was followed by reminder postcard a week later. Second and third questionnaires were mailed to nonrespondents at four weeks and seven weeks after the initial intercept.

Data Analysis

SPCM is based on the utility maximization theory that individuals make trip choices that maximize their satisfaction (i.e., utility) in consideration of the relative importance of the various attributes. Furthermore, according to random utility theory (see McFadden 1974), utility consists of a deterministic component and a random error component because of uncertainty factors not observed by a researcher. Researchers can only assess utility using the quantifiable section of utility (i.e., the observed deterministic component of utility for the set of attributes included). The existence of the random error component (i.e., the effect of unobserved influences) indicates that utility can only be inferred from individuals’ observed choices. This random error leads to the use of the indirect utility function

U_{j} = μ β X + ε_{j}

where U_j is the utility of an alternative beach trip j,µ is a scale parameter, X is the vector of the attributes presented in paired choice sets, β is the coefficient vector (or parameter estimates) to be estimated, and ϵ_j is the unobservable error component of utility. Assuming the error component is independently and identically distributed with a type I extreme-value distribution (i.e., Gumbel-distributed), the model specification can result in a conditional logit model (Ben-Akiva and Lerman 1985).

A nonnegligible property of a conditional logit model is the satisfaction of the independence of the irrelevant alternatives (IIA) property. The IIA property, derived from the error assumption of the conditional logit model, suggests that the ratio of two probabilities of any two alternatives for an individual is not affected by the presence or absence of any other alternatives in a choice set (Ben-Akiva and Lerman 1985; Louviere, Hensher, and Swait 2000). Because conditional logit estimates are biased in case of IIA violation, a popular way to deal with violation of the IIA property is the use of random parameter logit modeling (Train 2003). A random parameter logit (RPL) model, which takes into account unobserved heterogeneity in the systematic part of the model by letting parameter coefficients vary over individual participants (i.e., capturing individual variability through random parameter coefficients), does not rely on the IIA property (see more of the RPL from Train 2003). As a result, for model estimation, the RPL is applied in this study.

Dummy coding was used for all attributes except trip cost. Model values for trip costs were determined by multiplying the cost of the respondents’ daily trip cost (asked elsewhere in the survey) by 1.20, 1, or 0.80 (±20%) in a manner congruent with the trip cost attribute in the choice sets. The alternative specific constant (ASC) was inserted to account for the influence of unobserved attributes not included in the model (Ben-Akiva and Lerman 1985). The positive value for ASC indicates that tourists were more favorable toward taking trips to coastal destinations than the alternative option of not taking a trip. In addition, interaction terms of the ASC and demographic/trip information (age, income, gender, and trip satisfaction) were used to increase model accuracy.

Results

Study Sample

A total of 1,735 tourists agreed to provide their names and addresses for follow-up mail surveys. We received 797 completed surveys and had a raw response rate of 44.2%. Because of nonresponses to different variables used in data analysis, 100 responses were deleted and the final data analysis was conducted with 697 respondents. Of 697 respondents, 239 were tourists to Myrtle Beach, 239 to Charleston, and 219 to Hilton Head Island. A majority of respondents were female (62%). The average age of respondents was 54 and respondents had an average of 20.7 paid vacation days per year. More than one-quarter (28%) of respondents had a household income higher than $100,000, and more than half had a college education (56%). Most respondents (94%) were Caucasian. On average, the respondents stayed 5.74 days on the coast and spent $71.89 per person per day on vacation.

Aggregate Model

The results of the RPL model are presented in Table 1. The RPL was estimated with 500 Halton draws using NLogit 4. Each attribute parameter, except for the attribute of “Trip cost,” was allowed to be normally distributed. It is conventional that the attribute of “Trip cost” is treated as a fixed variable for the benefits of model estimation and the assumption that the distribution of marginal willingness to pay for an attribute follows the distribution of that attribute’s coefficient (Phanikumar and Maitra 2006). Furthermore, to reflect observed heterogeneity of tourists’ preferences, four individual-specific variables were included by interacting them with the ASC.

Table 1.

Results of RPL Model (Aggregated Model).

Attributes	Levels	Mean Coefficients	Standard Deviation of Coefficient
ASC		−0.4724 (0.772)	1.737** (0.126)
Destination	Small town setting	0.2901** (0.117)	1.283** (0.183)
	Resort setting	0.7059** (0.119)	1.669** (0.167)
	State park setting	−0.2699* (0.151)	2.493** (0.226)
Availability of activities	Medium	0.9584** (0.098)	0.209 (0.294)
	High	1.0103** (0.097)	0.302 (0.317)
Activities’ local character	Some emphasis	0.5380** (0.092)	0.051 (0.328)
	High emphasis	0.8361** (0.097)	0.641** (0.194)
Restaurant quality	Three stars	1.3565** (0.104)	0.228 (0.219)
	Four stars	1.7194** (0.116)	0.894** (0.641)
Restaurant ownership	Mix	−0.1816** (0.089)	0.419* (0.236)
	National	−0.9100** (0.094)	0.499** (0.239)
Trip cost		−0.0251** (0.002)
Age*ASC		−0.0340** (0.008)
Income*ASC		−0.0265 (0.191)
Gender*ASC		0.4078** (0.199)
Satisfaction*ASC		0.2043 (0.167)
Log likelihood		−3426.9
McFadden ²		0.187

Note: Standard errors are in parentheses.

p ≤ 0.10. **p ≤ 0.05.

The explanatory power of the all-tourist model measured by McFadden’s ρ², which is equivalent to the R² in a conventional regression model (Greene 2000), was 0.187. All of the main attributes were statistically significant (Table 2). The statistically significant coefficients of the destination attribute together indicate that the degree of destination development was important for tourists’ destination choice. The positive signs of “Small town setting” and “Resort setting” denote that more tourists preferred these settings compared to “Urban setting” as a base level; however, the relatively high standard deviations of the coefficients for this attribute indicate a high degree of individual variability in preference of destinations. The negative sign of “State park setting” shows that tourists were less likely to prefer this destination setting compared to the base destination level (i.e., urban setting). The positive coefficients on the attribute of “Availability of activities” signify that the availability of other tourism activities at or near the destination had a significant influence on a tourist’s destination choice. The positive signs of the activities’ local character attribute (“Medium” and “High”) show a strong preference for tourism activities that possess a local character unique to the South Carolina coast. The positive coefficients of the two levels (“Three stars” and “Four stars”) on the restaurant quality attribute suggest a high quality of dining experience was important to tourists. Likewise, the negative coefficients of “Restaurant ownership” jointly show tourists preferred locally owned restaurants at their destination to national chains or a mix of locally owned restaurants and national chains. The negative sign of “Trip cost” corresponded with our prior expectation that the quantity demanded (i.e., trip demands) is negatively correlated with the price of a good or service (i.e., trip cost). The two attributes involving local heritage (“Activities’ local character” and “Restaurant ownership”) were both significant but were slightly smaller than their counterparts that did not involve local heritage (“Availability of activities” and “Restaurant quality”), indicating that they were slightly less influential on decision making. Two interaction variables of Age*ASC and Gender*ASC were significant, meaning that younger male tourists were more interested in taking trips to the coastal destinations. Finally, the statistical significance of standard deviations for a number of parameter estimates in the last column of Table 1 denotes tourist heterogeneous preferences and thus supports within-group segmentation.

Table 2.

Results of RPL Models by Trip Destination.

		Myrtle Beach		Charleston		Hilton Head
Attributes	Level	Mean Coefficient	Standard Deviation of Coefficient	Mean Coefficient	Standard Deviation of Coefficient	Mean Coefficient	Standard Deviation of Coefficient
ASC		0.9462 (1.405)	1.764** (0.215)	−1.3107 (1.201)	1.505** (0.198)	−1.5882 (1.636)	1.764** (0.247)
Destination	Small Town setting	0.1232 (0.213)	1.554** (0.317)	0.2880 (0.200)	1.279** (0.310)	0.7186** (0.237)	1.367** (0.359)
	Resort setting	0.2436 (0.184)	1.415** (0.278)	0.4189** (0.207)	1.826** (0.298)	1.8356** (0.272)	1.713** (0.341)
	State park setting	−0.2766 (0.275)	2.736** (0.409)	−0.5495** (0.238)	2.091** (0.331)	0.1250 (0.334)	3.001** (0.537)
Availability of activities	Medium	0.9495** (0.178)	0.250 (0.600)	1.0810** (0.168)	0.124 (0.312)	0.8810** (0.201)	0.550 (0.474)
	High	1.0487** (0.171)	0.341 (0.629)	1.1436** (0.169)	0.432 (0.355)	0.9222** (0.192)	0.177 (0.511)
Activities’ local character	Some emphasis	0.5721** (0.162)	0.220 (0.337)	0.4726** (0.158)	0.500 (0.454)	0.7363** (0.190)	0.132 (0.749)
	High emphasis	0.8316** (0.172)	0.815** (0.315)	0.9289** (0.160)	0.493 (0.366)	0.8530** (0.203)	0.762* (0.399)
Restaurant quality	Three stars	1.3698** (0.184)	0.011 (0.309)	1.4601** (0.179)	0.239 (0.330)	1.4315** (0.208)	0.262 (0.395)
	Four stars	1.6197** (0.206)	1.103** (0.279)	1.9914** (0.203)	0.834** (0.304)	1.7648** (0.237)	1.109** (0.319)
Restaurant ownership	Mix	−0.0784 (0.156)	0.157 (0.580)	−0.2251 (0.152)	0.635** (0.282)	−0.3546* (0.182)	0.720** (0.347)
	National	−0.9502** (0.169)	0.345 (0.520)	−1.0280** (0.157)	0.343 (0.384)	−0.9658** (0.210)	1.107** (0.309)
Trip cost		−0.0276** (0.004)		−0.0185** (0.004)		−0.0357** (0.005)
Age*ASC		−0.0710** (0.015)		−0.0266** (0.011)		−0.0177** (0.016)
Income*ASC		0.0601 (0.330)		−0.2306 (0.310)		0.1258 (0.378)
Gender*ASC		0.4257 (0.343)		1.0913** (0.325)		−0.3614 (0.398)
Satisfaction*ASC		0.4146 (0.266)		0.2737 (0.230)		0.1201 (0.303)
Log likelihood		−1159.0		−1186.0		−1032.5
McFadden ²		0.200		0.196		0.190

Note: Standard errors are in parentheses.

p ≤ 0.10. **p ≤ 0.05.

Segmented Model

Turning to the models segmented by the destination (where the tourists were intercepted during the survey), all models show good fit to the data with the explanatory power between 0.190 and 0.200 (Table 2). Each group shows a similar pattern of preferences, with identical signs for the parameter coefficients for the following attributes: Availability of activities, Activities’ local character, Restaurant quality, and Trip cost. The same interpretation explained above can be applied to each of these attributes. Nevertheless, the three destination models also show some differences in tourist preferences for the management attributes. For example, while tourists to Beaufort/Hilton Head region were more interested in visiting tourism destinations of small town setting or resort setting, those to Myrtle Beach seemed to be indifferent to those tourism destinations. Likewise, tourists to Beaufort/Hilton Head region preferred to dine in locally owned businesses compared to the two options but those to Myrtle Beach and Charleston were indifferent to the option of a mix of locally owned businesses.

In order to help better understand tourist preference for each destination attribute, the parameter coefficients can be converted to monetary values, called marginal willingness to pay (MWTP). The MWTP can be obtained first by dividing the coefficient of each attribute by the coefficient of trip cost and then multiplying by per person per day expenditures. The standard errors of the MWTPs were generated using the Krinsky and Robb procedure (Krinsky and Robb 1986). Table 3 reports the MWTPs and standard errors. For example, average tourists were willing to pay substantially more to experience a variety of tourism activities available in the destination ($30 for the level of “Medium” availability and $32 for “High”) compared to the base option of basic activities available.

Table 3.

Implicit Prices by Trip Destination.

		Aggregate	Myrtle Beach	Charleston	Hilton Head
Attributes	Level	$	$	$	$
Destination	Small town setting	9.04^NS (3.70)	2.51^NS (4.47)	17.24^NS (14.42)	13.81 (4.85)
	Resort setting	22.01^NS (3.95)	4.96^NS (3.81)	25.08 (14.91)	35.27 (7.16)
	State park setting	−8.42^NS (4.89)	−5.63^NS (6.01)	−32.90 (17.81)	2.40^NS (6.84)
Availability of activities	Medium	29.88 (4.10)	19.32 (5.31)	64.73 (19.83)	16.93 (4.77)
	High	31.50 (4.03)	21.33 (4.97)	68.47 (20.20)	17.72 (4.59)
Activities’ local character	Some emphasis	16.77 (3.28)	11.64 (3.79)	28.30 (11.88)	14.15 (4.21)
	High emphasis	26.07 (3.77)	16.92^a (3.98)	55.62 (15.63)	16.39^a (4.31)
Restaurant quality	Three stars	42.29 (4.89)	27.87^a (6.01)	87.42 (24.20)	27.51^a (5.10)
	Four stars	53.60 (5.90)	32.95^a (6.56)	119.24 (30.89)	33.91^a (5.89)
Restaurant ownership	Mix	−5.66 (2.73)	−1.59^NS (3.31)	−13.48^NS (9.83)	−6.81 (3.42)
	National	−28.37 (3.67)	−19.33^a (4.44)	−61.56 (17.39)	−18.56^a (4.28)

Note: Standard errors are in parentheses. The implicit prices were computed using average coastal trip expenditures per person per day. NS = not significant in Table 2.

Statistical test fails to reject the null hypothesis of no differences in implicit prices.

Heterogeneous preferences of those three tourist groups segmented by tourism destination were also examined. For comparison purpose, non-significant coefficients were also converted to MWTPs and included here. For example, tourists to Hilton Head Island were willing to pay $35 to visit a tourism destination in a resort setting, but those to Charleston and Myrtle Beach were less interested in the same type of a destination with the MWTP of $25 and $5, respectively. To more rigorously compare MWTPs at the destination level, the null hypotheses of no differences were tested among those three tourist groups using a standard hypothesis test (i.e., t test). The results indicate that all but two of the null hypotheses were rejected. The attributes of “Restaurant quality” and “Restaurant ownership” between tourists to Myrtle Beach and those to Hilton Head Island were not significantly different (Table 3). As a result, the MWTPs estimated were considerably different among the different tourists groups, and in general, tourists to Charleston had higher MTWP values.

Discussion

Aggregated together, tourists preferred a resort setting above all others and the State Park setting the least. Myrtle Beach tourists did not display a strong preference for destination type, while Charleston tourists preferred a resort and state park setting over an urban environment, which was surprising given the urban nature of Charleston. The availability of activities showed a similar trend across the three regions. The increase from a low level to medium produced a significant increase in MWTP but a further increase to a high level produces only a slight increase.

Preferences for activities’ local character showed two different patterns. In Myrtle Beach and Hilton Head, increase from low character to some character produced a much larger increase in MWTP than increasing from some character to high character. Charleston tourists showed different preferences with the increase in MWTP from low to some character being roughly equal to the increase in MWTP from some to high character. Restaurant quality was found to be the most important attribute for South Carolina tourists. This result points to the importance of the culinary experience to tourists. The increase from “two stars” to “three stars” produced a much larger increase in MWTP than the increase from “three stars” to “four stars.” Restaurant ownership showed similar patterns of preference across the three destinations. In all destinations, a change from national to mix produced a much larger increase in MWTP than a change from mix to local. The MWTP on all of the attributes with ordinal levels (availability of activities, activities’ local character, restaurant quality, and restaurant ownership) were higher for the increase from zero to level one than from level one to level two.

Tourists in Charleston had the overall highest MWTP for all attributes and levels except destination type. The results show a clear demand for heritage elements in coastal destinations. There was a statistically significant MWTP for local character in tourism activities and local ownerships (vs. only national) of restaurants. However, the MWTP for these heritage-related elements is less than the non-heritage-related elements, namely, availability of activities and restaurant quality. As might be expected, the demand for locally owned restaurants and local character in activities was highest in Charleston, the destination most commonly associated with culture and heritage tourism. However, tourists to all three destinations showed a demand for locally owned restaurants and local character in the activities.

The practical relevance of these results is that while these cultural elements are not the major driving factors behind destination choice, they still influence destination choice. Even in destinations that are not known for showcasing traditional culture, heritage and authenticity are important parts of a vacation. Developers should be conscious of local heritage when planning the future of destinations and attractions. In addition, it should be noted that food is an extremely important part of the South Carolina coastal vacation. Restaurant quality had the highest coefficient in the aggregate model, and restaurant ownership was a significant factor as well. These results were consistent in all destinations after segregating the model.

Conclusion

This study used a SCPM method to evaluate the importance of a variety of attributes in a consumer’s choice of coastal tourism destinations. While numerous studies have analyzed consumer demand for authentic or heritage elements in tourism products (Kerstetter, Confer, and Graefe 2001; Sedmak and Mihalic 2008), few have compared this demand to other attributes to gain a relative importance of the demand for cultural elements in a vacation (McKercher and Chan 2005). Results show that heritage related attributes were significant factors in decision making; however, they were not as important as more traditional elements such as destination type and availability of activities. These results are consistent when the tourists were aggregated or segmented by destination. Even in Charleston, a destination known for showcasing heritage, consumer demand for heritage-related elements remained relatively low. At the same time, the results show that even in Myrtle Beach, a destination known for its garishness, there is a demand for heritage items. One limitation of this paper should be noted; the SPCM technique as implied in the name is a stated preference method that may suffer from hypothetical bias, meaning that respondents are likely to inflate their MWTP values as they will not actually have to pay the listed amount. Therefore, reported MWTP values may be higher than actual values.

A primary goal of tourism management is developing and sustaining destinations that match consumer preferences. This research shows that heritage elements are an important feature of South Carolina coastal tourism, and has two main practical applications. For tourism planners, this shows that even in destinations not noted for showcasing local heritage and culture, the local character of a destination is still important to tourists. The unique personality of the South Carolina Coast should be emphasized, even to 3S type destinations such as Myrtle Beach. For regional developers, this research highlights the possible value of linking traditional coastal industries to tourism. Long-term vitality of traditional coastal industries could possibly be enhanced through strategies that deliberately integrate these assets with tourism development. Tourism may represent a new avenue for development in industries or destinations that are not thought of as having traditional ties to tourism. It is hoped that a symbiotic relationship between tourism and these traditional industries can be created to increase the viability.

At the same time, these results provide some evidence to support McKercher and Chan’s (2005) argument that cultural tourism has been overblown. The local character element of the South Carolina vacations, while statistically significant, was a relatively minor factor in determining visitor preferences. This is true even in Charleston, a destination popular largely because of its ability to replicate the feel of the old south. These results may indicate that the cultural elements of vacation activities are of relatively minor importance. At the same time, results indicate that the importance of the culinary experience should not be underestimated. The MWTP values of restaurant quality were generally higher than that of availability of activities and the MWTP of local restaurant ownership are generally higher than those of activities’ character. In all locations, the dining experience seemed to be an extremely important part of the South Carolina vacation experience. The similarities of the coefficients for restaurant ownership across the destinations suggest that tourists felt that regional coastal cuisine is an important part of their vacation, no matter what type of coastal destination they choose. Tourism planners and markets should work to ensure that high-quality dining experiences continue to be developed for and marketed to tourists.

Further research along this line may provide more evidence as to the importance of cultural/heritage element at a destination. This study found some evidence to support McKercher and Chan’s (2005) argument that heritage tourism may be overemphasized, and that cultural elements "complement trip experiences but are demonstrably not the hub around which a travel” is planned (p. 30). Future work from these data may include a segmentation by motivations, and perhaps a segment of tourists with a high demand for local character could be identified; at the same time a segment that disregards local heritage may be found. Similarly, a culinary tourist–based segment may be identified through this analysis; the size and demographic of this analysis may provide information to tourism developers and marketers. The issue of the increasing segmentation of tourists into different niches is debatable and occasionally contentious and deserves further attention. Determining whether different segments of tourists have different preferences will provide some evidence as to how much segmentation is too much. In addition, this study did not pursue the influences that different cultural meanings might have on preferences. Tourists often have very different perceptions of heritage sites (Lin, Morgan, and Coble, 2013). South Carolina has a number of sites such as Fort Sumter (where the American Civil War began) or plantations that were once operated with slave labor, which may hold vastly different meanings to different tourists, and these different meanings may in turn influence destination choice in different ways. Further analyses of the varying perceptions of these heritage sites may lead to a better understanding of how they influence destination choice.

While this study was conducted in South Carolina, the survey included tourists from across the United States and even included international tourists. This large sample indicates a broad desire for cultural elements in vacations that should be recognized and accounted for in tourism destinations of all types. As a whole, these results illuminate consumer preferences for tourism along the South Carolina Coast and should provide practical information for developers and marketers. The continued evaluation of consumer preferences in necessary to ensure that new developments are meeting the changing demands of tourists. Evaluation of demand is especially important given that researchers are observing trends toward a new type of tourists who demand unique and authentic experiences (Gale 2005; Poon 1993).

Footnotes

Acknowledgements

This paper was prepared by Clemson University under NA06OAR4170015, Am. 9. The statements, findings, conclusions, and recommendations are those of the authors and do not necessarily reflect the views of the South Carolina Sea Grant Consortium or the National Oceanic Atmospheric Administration.

Acknowledgements

We would like to thank the reviewers and editor for their many helpful comments that greatly aided in the development of this manuscript.

Declaration of Conflicting Interests

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

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author Biographies

R. Geoffrey Lacher is an assistant Professor in the School of Community Resources and Development at Arizona State University. His research interests include the economics of tourism, consumer demand for sustainable tourism certifications, tourism in the developing world, the economics of souvenirs, and choice modeling. He has previous research experience in Thailand, the Dominican Republic, and Tanzania as well as the United States.

Chi-Ok Oh is an assistant professor in the Department of Community, Agriculture, Recreation and Resource Studies at Michigan State University. His research interests are in tourism and recreation economics and human dimensions of natural resources, with a particular interest in estimating social and economic benefits of tourism activities and related natural resources. His research delves into environmental, economic and sociopolitical sustainability by asking how people view, value and use natural resources, particularly in the areas of parks and protected areas. His scholarly goal is to provide research that contributes to understanding how conservation attitudes and preferences develop, particularly related to recreation and tourism involvement.

Laura W. Jodice is a Research Associate for the International Institute for Tourism Research and Development at Clemson University. Her research focuses on developing educational and outreach strategies that build understanding of marine fisheries and aquaculture and investigating how tourism markets can promote communications about sustainable fisheries practices and build economic diversification in fisheries communities.

William C. Norman is a Professor in Clemson University's Department of Parks, Recreation and Tourism Management and the former Director of the Recreation, Travel and Tourism Institute. His research is focused on leisure travel decision making, recreation behavior, leisure constraints, tourism marketing and the study of niche travel markets (i.e., agritourism, heritage tourism, sport tourism, culinary tourism, and nature-based tourism). His current research examines the contribution and impact of tourism to the sustainability of South Carolina's rural and coastal communities.

References

Advisory Council on Historic Preservation. (2008). “Web-Available Studies on the Economic Impacts of Historic Preservation.” http://www.achp.gov/economicstudies.html (accessed July 10, 2012).

Agarwal

Sheela

. (2002). “Restructuring Seaside Tourism.” Annals of Tourism Research, 29 (1): 25-55.

Alliance of National Heritage Areas. (2005). “Economic Impact of Heritage Tourism Spending.” http://www.nationalheritageareas.com/documents/ANHA_Eco_Imp_Report_2005_MGM2.pdf (accessed July 10, 2012).

Barkley

David L.

Henry

Mark S.

Gantt

Brian P.

(2004). “The South Carolina Shrimp Trawling Industry 2003 Survey Results.” http://cherokee.agecon.clemson.edu/redrl_rpt12.pdf (accessed September 1, 2011).

Ben-Akiva

Moshe

Lerman

Steven R.

(1985). Discrete Choice Analysis: Theory and Application to Travel Demand. Cambridge, MA: MIT Press.

Bennett

Jeff

Adamowicz

Vic

. (2001). “Some Fundamentals of Environmental Choice Modeling.” In The Choice Modeling Approach to Environmental Valuation, edited by Bennett

Jeff

Blamey

Russell

. London: Edward Elgar, pp. 37-69.

Boley

Bynum

Nickerson

Norma

Bosak

Keith

. (2011). “Measuring Geotourism: Developing and Testing the Geotraveler Tendency Scale (GTS).” Journal of Travel Research, 50 (5), 567-78.

Bowitz

Einar

Ibenholt

Karin

. (2009). “Economic Impacts of Cultural Heritage—Research and Perspectives.” Journal of Cultural Heritage, 10 (1): 1-8.

Bridaa

Juan Gabriel

Meleddub

Marta

Pulinac

Manuela

. (2012). “Understanding Urban Tourism Attractiveness.” Journal of Travel Research, 51 (6): 730-41.

10.

Carey

Sandra

Gountas

Gilbert

(1997). “Tour Operators and Destination Sustainability.” Tourism Management, 18 (7): 425-31.

11.

Che

Deborah

Veeck

Gregory

Veeck

Ann

. (2005). “Agritourism and the Selling of Local Food Production, Family and Rural American Traditions to Maintain Family Farming Heritage.” In Rural Change and Sustainability: Agriculture, the Environment and Communities, edited by Essex

Stephen

Gilg

Andrew

Yarwood

Richard

Smithers

John

Wilson

Randall

. Cambridge: CABI, pp. 107-21.

12.

Cincin-Sain

Biliana

Knecht

Robert W.

(1998). Integrated Coastal and Ocean Management. Washington, DC: Island Press.

13.

Cohen

Erik

Avieli

Nir

. (2004). “Food in Tourism.” Annals of Tourism Research, 31 (4): 755-78.

14.

Dahles

Heidi

. (1998). “Redefining Amsterdam as a Tourist Destination.” Annals of Tourism Research, 25 (1): 55-69.

15.

Daugstad

Karoline

. (2008). “Negotiating Landscape in Rural Tourism.” Annals of Tourism Research, 35 (2): 402-26.

16.

Deale

Cynthia

Norman

William C.

Jodice

Laura W.

(2008). “Marketing Locally Harvested Shrimp to South Carolina Coastal Visitors: The Development of a Culinary Tourism Supply Chain.” Journal of Culinary Science and Technology, 6 (1): 5-23.

17.

Dillman

Don A.

(2000). Mail and Internet Surveys: The Tailored Design Method. New York: John Wiley.

18.

Hall

C. Michael

. (2001). “Trends in Ocean and Coastal Tourism: The End of the Last Frontier?” Ocean and Coastal Management, 44 (9-10): 601-18.

19.

Herrero

Luis C.

Sanz

José Ángel

Devesa

María

Bedate

Ana

Barrio

María José del

. (2006). “The Economic Impact of Cultural Events: A Case-Study of Salamanca 2002, European Capital of Culture.” European Urban and Regional Studies, 13 (1): 41-57.

20.

Gale

Tim

. (2005). “Modernism, Post-Modernism and the Decline of British Seaside Resorts as Long Holiday Destinations: A Case Study of Rhyl, North Wales.” Tourism Geographies, 7 (1): 86-112.

21.

Gallup

John Luke

Sachs

Jeffrey D.

Mellinger

Andrew D.

(1999). “Geography and Economic Development.” International Regional Science Review, 22 (2): 179-232.

22.

Gormsen

Erdmann

. (1997). “The Impact of Tourism on Coastal Areas.” GeoJournal, 42 (1): 39-54.

23.

Greene

William H.

(2000). Econometric Analysis. 4th edition. Upper Saddle River, NJ: Prentice Hall.

24.

Johnson

Jeffrey C.

Orbach

Michael K.

(1990). “Migratory Fishermen: A Case Study in Interjurisdictional Natural Resource Management.” Ocean and Shoreline Management, 13 (3-4): 231-52.

25.

Kerstetter

Deborah L.

Confer

John J.

Graefe

Alan R.

(2001). “An Exploration of the Specialization Concept within the Context of Heritage Tourism.” Journal of Travel Research, 39 (3): 267-74.

26.

Kildow

Judith T.

McIlgorm

Alistair

. (2010). “The Importance of Estimating the Contribution of the Oceans to National Economies.” Marine Policy, 34 (3): 367-74.

27.

Krinsky

Itzhak

Robb

A. Leslie

(1986). “On Approximating the Statistical Properties of Elasticities.” The Review of Economics and Statistics, 68 (4): 715-19.

28.

Kuhfeld

Warren F.

Tobias

Randall D.

(2005). “Large Factorial Designs for Product Engineering and Marketing Research Applications.” Technometrics, 47 (2): 132-41.

29.

Lacher

R. Geoffrey

Harrill

Rich

. (2010) “Going beyond Sun, Sand, and Surf? An Importance-Performance Analysis of Activities in a 3S Resort Destination.” e-Review of Tourism Research, 8 (4): 57-68.

30.

Lin

Hui-Nien (Sylvia)

Morgan

Mark

Coble

Theresa

. (2013). “Remember the Alamo: A Cross-Cultural Analysis of Visitor Meanings.” Journal of Travel Research, 52 (1) : 42–55.

31.

Littrell

Mary A.

Anderson

Luella F.

Brown

Pamela J.

(1993). “What Makes a Craft Souvenir Authentic?” Annals of Tourism Research, 20 (1): 197-215.

32.

Louviere

Jordan J.

(1988). “Conjoint Analysis Modeling of Stated Preferences.” Journal of Transport Economics and Policy, 22: 93-119.

33.

Louviere

Jordan J.

Hensher

D. A.

Swait

J. D.

(2000). Stated Choice Methods: Analysis and Applications. Cambridge: Cambridge University Press.

34.

McFadden

Daniel

. (1974). “Conditional Logit Analysis of Qualitative Choice Behavior.” In Frontiers in Econometrics, edited by Zarembka

New York: Academic Press, pp. 105-42.

35.

McKercher

Bob

Chan

Andrew

. (2005). “How Special Is Special Interest Tourism?” Journal of Travel Research, 44 (1): 21-31.

36.

Nicolau

Juan L.

(2011). “Differentiated Price Loss Aversion in Destination Choice: The Effect of Tourists’ Cultural Interest.” Tourism Management, 32 (5): 1186-95.

37.

Palmer

Catherine

. (1999). “Tourism and the Symbols of Identity.” Tourism Management, 20 (3): 313-21.

38.

Phanikumar

C. V.

Maitra

Bhargab

. (2006). “Modeling Willingness-to-Pay Values for Rural Bus Attributes under Different Trip Purposes.” Journal of the Transportation Research Forum, 45 (2): 31-44.

39.

Poon

Auliana

. (1993). Tourism, Technology and Competitive Strategies. Oxon: CAB International Wallingford.

40.

Poria

Yaniv

Butler

Richard

Airey

David

. (2001). “Clarifying Heritage Tourism.” Annals of Tourism Research, 28 (4): 1047-49.

41.

Poria

Yaniv

Reichel

Arie

Cohen

Raviv

. (2011). “World Heritage Site—Is It an Effective Brand Name?” Journal of Travel Research, 50 (5): 482-95.

42.

Porter

Michael E.

(1998). The Competitive Advantage of Nations: With a New Introduction. New York: Free Press.

43.

Priestley

Gerda

Mundet

Lluís

. (1998). “The Post-Stagnation Phase of the Resort Cycle.” Annals of Tourism Research, 25 (1): 85-111.

44.

Richards

Greg

. (1996). “Production and Consumption of European Cultural Tourism.” Annals of Tourism Research, 23 (2): 261-83.

45.

SAS Institute Inc. (2004). SAS/QC Software: User’s Guide, Version 9.1. Cary, NC: SAS Institute.

46.

SC Department of Parks, Recreation and Tourism. (2012). “South Carolina: Made for Vacation.” http://www.discoversouthcarolina.com/ (accessed February 1, 2012).

47.

Sedmak

Gorazd

Mihalic

Tanja

. (2008). “Authenticity in Mature Seaside Resorts.” Annals of Tourism Research, 35 (4): 1007-31.

48.

Sharbaugh

R. Kevin

. (2011). “Take Me to the Water: Florida's Shrinking Public Access to the Waterfront and Steps to Preserve It.” Sea Grant Law and Policy Journal, 4 (1): 4-27.

49.

Silberberg

Ted

. (1995). “Cultural Tourism and Business Opportunities for Museums and Heritage Sites.” Tourism Management, 16 (5): 361-65.

50.

Strauss

Charles H.

Lord

Bruce E.

(2001). “Economic Impacts of a Heritage Tourism System.” Journal of Retailing and Consumer Services, 8 (4): 199-204.

51.

Smythe

Tiffany C.

(2010). “Can Coastal Management Programs Protect and Promote Water-Dependent Uses?” Coastal Management, 38 (6): 665-80.

52.

Telfer

David J.

Wall

Geoffrey

. (1996). “Linkages between Tourism and Food Production.” Annals of Tourism Research, 23 (3): 635-53.

53.

Torres

Rebecca

. (2003). “Linkages between Tourism and Agriculture in Mexico.” Annals of Tourism Research, 30 (3): 546-66.

54.

Tourism Development International Ltd. (2006). “South Carolina Tourism Action Plan.” http://www.newcarolina.org/UserFiles/publications/TourismActionPlanOct2006.pdf (accessed September 1, 2011).

55.

Train

Kenneth E.

(2003). “Mixed Logit.” In Discrete Choice Methods with Simulation, edited by Train

Kenneth

. New York: Cambridge University Press, pp. 134-50.

56.

U.S. Travel Association of America. (2009). “The Economic Impact of Travel on South Carolina 2008. A Study Prepared for the South Carolina Department of Parks, Recreation and Tourism.” www.scprt.com/files/Research/EconomicImpactofTravelonSCCounties2008.pdf (accessed September 1, 2011).

57.

Urry

John

. (2000). Sociology beyond Societies: Mobilities for the Twenty-First Century. London: Routledge.

58.

Walker

Kenneth

Arnn

Matt

. (1998). “Preserving Waterfronts for Water Dependent Uses.” National Oceanic and Atmospheric Administration, State of the Coast Report. http://state_of_coast.noaa.gov/bulletins/html/wdu_11/wdu.html (accessed September 1, 2011).

59.

Warnken

Jan

Russell

Roslyn

Faulkner

Bill

. (2003). “Condominium Developments in Maturing Destinations: Potentials and Problems of Long-Term Sustainability.” Tourism Management, 24 (2): 155-68.

60.

Yale

Pat

. (1991). From Tourist Attractions to Heritage Tourism. London: ELM Publications.