Understanding Urban Tourism Attractiveness

Abstract

The purpose of this investigation is to analyze to what extent a museum may be regarded as a potential driver for urban tourism. Via a travel cost model, the likelihood to revisit South Tyrol’s Museum of Archaeology (the Ötzi museum), in the Autonomous Province of Bolzano (Italy), is estimated and the factors that influence visitors’ experience, motivations, and their intention to revisit the museum are analyzed. To this aim, the number of actual visits to the museum is used as an economic indicator of museum attractiveness. The findings show that the museum has an important educative role in addition to being an economic earner for the city. Nevertheless, entrance fees discourage repeat visit, particularly for those on a low income. The findings also provide a tool to manage urban heritage resources and plan future urban developments around the Ötzi museum.

Keywords

museum travel cost repeat visits zero-truncated Poisson urban tourism

Introduction

One of the recurrent questions in urban tourism literature regards which factors attract visitors to a city. Cultural facilities, such as museums and art galleries, have long been identified as “main attractions” (Law 1993, after Jansen-Verbeke 1988). These cultural assets can be regarded as unique and nonreplicable public goods that play a relevant role for local communities as a reminder of their cultural past and present social cohesion.

Their importance therefore is not to be underestimated and, increasingly, the European Commission (2006) has promoted a policy aimed at enhancing education, vocational training, youth, and culture based on tourism activities. On a broad level, the leveraging of cultural assets can be divided on the basis of communities’ size. For small communities, recently, there has been an upsurge in the number of abandoned small towns restored to historical culture sites, as a means to activate a virtuous path of growth for local communities. Residents and businesses can benefit from higher levels of revenue, employment, income, and knowledge, all of which can revitalize a given region and often mitigate against youth migration, particularly common in economically marginal areas (Rudan 2010). For larger communities, new “cultural” buildings have been constructed as a symbol of regional/national identity, cultural engagement, and economic development, helping the city regain a central position and creating a rejuvenation phase for destinations previously characterized by a mature or declining phase in the economic life cycle. A recent outstanding example of this latter case is the Opera House in Oslo, Norway (Smith and Strand 2011).

Museums have a key role as repositories of historical knowledge, custodians of historical identity and community roots and are therefore natural tools to educate the “present.” They can be regarded as a stimulus for the local economy. Culture produces positive externalities as culture consumers generally have a higher spending propensity than other consumer segments (Europa Inform 2004). Culture enriches society’s pool of knowledge, enhancing efficiency in human capital.

Hence, it is of interest to investigate how local heritage and in particular a museum can enhance the economy and the attractiveness of the city, of Bolzano, in which it is located as a tourism destination. To this aim, the present paper develops a model to predict repeat visitation to South Tyrol’s Museum of Archaeology in Bolzano (Italy). This museum documents the ancient history of South Tyrol and presents outstanding findings from the prehistoric periods—Stone Age, Bronze Age, and the Iron Age—as well as Roman times and through to the middle ages at the time of Charlemagne (around ad 800). Within this museum, the Iceman (best known as “Ötzi”) occupies a central position in the exhibition area and is without doubt one of the main attractions. This discovery dates back to 1991, when this intact glacier mummy from more than 5,000 years ago was found at 92.56 meters above the sea level in the Schnal Valley glacier in the Italian Alps together with a number of accompanying artifacts (clothing and equipment). This was an extraordinary find that has provided a unique insight into the life of this man who was climbing at high altitudes and of life in the Chalcolithic Period.

In order to establish to what extent this museum and its main attraction may be regarded as a pull factor for the city of Bolzano, an investigation on visitors’ preferences and behavior is carried out via a travel cost model. To this aim, the number of visits to the museum is used as an economic indicator that measures the attractiveness of the city of Bolzano. The relevant data were obtained from face-to-face interviews undertaken in the museum during the summer of 2010 (June-August). The representative sample consists of 724 museum visitors. Empirically, a zero-truncated Poisson is estimated, where the dependent variable is given by the number of times the respondent visited the museum in the past. As far as the authors know, this econometric approach is used for the first time to investigate the likelihood to revisit a museum. From a policy perspective, it is of great importance to investigate what the main determinants are for repeat visitation to a specific site. The empirical findings provided in this paper give destination managers and policy makers valuable information to formulate management and marketing strategies for future repeat visits. Such insight allows, on one hand, businesses to plan their activities in a more efficient manner, and on the other hand, local institutions to stimulate urban planning policies around this exclusive museum, thus also enhancing the performance of urban tourism.

The paper is organized as follows. In the following section, a literature review is provided. In the third section, the methodological framework is highlighted while the fourth section provides a description of the case study. And in the fifth section, an account of the empirical findings is given. Discussion and concluding remarks are provided in the last section.

Literature Review

Since the 1980s, a strand of the tourism literature has concentrated on assessing the urban tourism phenomenon in a systematic way (Edwards, Griffin, and Hayllar 2008). In a comprehensive literature review on urban tourism, Ashworth and Page (2011) pointed out that urban tourism represents an important form of tourism worldwide. Nevertheless, the main question is still to determine which leading factors attract visitors to a city. The answer is not straightforward given that even a simple definition of urban tourism does not exist, as it embeds a multifaceted set of activities without well-defined boundaries (Edwards, Griffin, and Hayllar 2008; Ashworth and Page 2011).

Following the Jansen-Verbeke (1988) framework, Law (1993) attempts to delineate the main urban pull factors. Specifically, the elements that determine the attractiveness of a city for its visitors are distinguishable in three key components: primary, secondary, and additional elements. Primary elements are cultural facilities (i.e., museums and art galleries), amusement and sport amenities, as well as physical and socio-economic characteristics, which represent the main purpose for tourists to visit a city. Secondary elements (i.e., accommodation and shopping) and additional elements (i.e., transportation and tourist information) are also very important for the success of urban tourism, although they are not the main attractors for visitors. “Additional elements” also feature parking facilities, information offices, guides, and signposts. On the whole, a city is no longer seen as a production unit but as a place of consumption. Residents and tourists are hence regarded as the new economic resource able to raise local income and produce social benefits, such as cultural and educational services for the local public (Park 2005).

The overall endowment of a city exerts a pull element for urban visitors, especially when the attractions are unique, as is usually the case with museums, which play a key role in attracting tourists in urban areas and thus contribute to the revitalization of the city. The literature mentions examples of this kind in Bilbao, Spain (Plaza 2000), Manchester, United Kingdom (Evans and Shaw 2004), Amsterdam, Holland, and Berlin, Germany (Aalst and Boogaarts 2004). Among other cultural attractions, museums have been defined as an efficient marketing tool for urban tourism (Jansen-Verbeke and van Rekom 1996) and as flagships of urban development (Hamnett and Shoval 2003), thus highlighting both their cultural dimension and their function as tourism promoters.

Recently, a thread of literature on heritage sites, which stems from environmental economics, states that the economic values of public goods, such as outdoor recreation resources and heritage sites, are not directly observable. Since the 1980s, economists have emphasized the need to assess the economic value of these assets to communities, as national and local governments often significantly contribute to their development and maintenance, raising tax credit (Ward and Loomis 1986). As a matter of fact, the efficient allocation of public resources is a critical objective of welfare economics. Across the decades, more advanced and sophisticated techniques have been applied to evaluate the monetary value of these nonmarket goods. Revealed Preference and Stated Preference techniques have been widely used (Mourato and Mazzanti 2002). On one hand, revealed preference techniques are applied to measure the value to the public of recreational or other public uses of the resource in terms of the changes in consumer surplus (e.g., services, activities). On the other hand, stated preferences techniques relate to nonusage values, which measure aspects of the resource’s value to individuals who are not linked to actual resource use (e.g., education, bequest, knowledge). Choi et al. (2010) emphasize that revealed preference methods, that is, travel cost analysis, hedonic price, averting behavior and market prices, can be used when data on market transactions and activities can be collected. Stated preference methods, such as contingent valuation and choice modeling analysis, can be used when these types of data are not available.

To date, there have been a large number of stated preference applications to evaluate the economic impact of museums (e.g., Mazzanti 2003; Sanz, Herrero, and Bedate 2003; Bedate, Herrero, and Sanz 2009; Colombino and Nene 2009; Lampi and Orth 2009; Choi et al. 2010); however, only a few studies have adopted revealed preference analysis to provide an economic valuation of museums and to measure their urban attractiveness. For example, Bedate, Herrero, and Sanz (2004) provide an application of travel cost to four heritage sites in Spain, including the museum of Burgos, which is characterized by a collection of archaeological items and fine arts. Boter, Rouwendal, and Wedel (2005) show how revealed preferences, in particular travel time, may be used for comparing the relative value of competing museums in the Netherlands. To this aim, they explicitly take into account the distance of the different museums from the population and the differences in willingness to travel. Fonseca and Rebelo (2010) use travel cost to estimate the demand curve in the Museum of Lamego (Portugal). They apply a standard Poisson model that reveals that the probability of visiting the museum is positively influenced by the education level and by being female and negatively affected by travel costs.

In the literature, several studies have also explored museum visitors’ preferences, motivation, satisfaction, and their probability to return and recommend the site. From an empirical perspective, several methodologies have been used, such as laddering techniques (Thyne 2001), ordinal and discrete logit models (Paswan and Troy 2004; Burton, Louviere, and Young 2009), factor and structural equation models (Harrison and Shaw 2004; Jeong and Lee 2006; de Rojas and Camarero 2008; Gil and Ritchie 2009; Hume 2011) as well as qualitative methods (Alcaraz, Hume, and Mort 2009; Packer and Bond 2010). Some important features can be drawn from the research in this field. Individuals have different values that influence their motivation to visit museums. However, together with education and learning objectives, socially oriented values, such as fun, entertainment, close relationships with other visitors, philanthropy, and social recognition play a role (Thyne 2001; Aalst and Boogaarts 2004; Paswan and Troy 2004). The exhibition environment, the variety of special exhibitions, and environmental cues (e.g., lighting, color, spaciousness, traffic flow) are important factors to perpetuate brand meaning and uniqueness in the minds of visitors (Jeong and Lee 2006; Bonn et al. 2008; Plaza 2008; Alcaraz, Hume, and Mort 2009). Burton, Louviere, and Young (2009) find that visitors tend to be actively engaged in social and cultural activities, often combining a number of activities in a single day. Hence, they suggest museums can benefit from strategic alliances with other cultural attractions and from joint packaging offers that add value to the overall experience.

Overall, although a vast literature is available on the impact that museums have on the local community, society, and economy (e.g., Luksetich and Partridge 1997; Plaza 2000; Maddison and Foster 2003; Dunlop et al. 2004; Maddison 2004; Stynes, Vander Stoep, and Sun 2004; Frey and Meier 2006; Scott 2006; Kinghorn, and Willis 2007, 2008; Plaza 2008; Çela, Lankford, and Knowles-Lankford 2009; Plaza and Haarich 2009; Fonseca and Rebelo 2010; Plaza 2010), as well as on visitors’ experience and stated preferences, only a few studies have adopted revealed preferences methods to examine the economic benefit deriving from museums’ activity, including the ability to enhance urban tourism. Hence, the present paper further contributes to the existing literature in the field by applying a zero-truncated Poisson approach within a travel cost theoretical framework (e.g., Scarpa, Thiene, and Tempesta 2007; Hellström and Nordström 2008; Martinez-Espiñeira et al. 2008).

Methodological Framework

Travel cost models are able to estimate economic use values associated with the heritage site, where a sample of visitors’ willingness to pay to visit the site is revealed (Throsby 2001). There are two types of travel costs: the Zonal Travel Cost and the Individual Travel Cost. The zonal travel cost, first presented by Hotelling (1949), is derived from the number of trips originating from a zone divided by the population of that zone, which is the dependent variable. This travel cost is normally used when multiple individual visits are infrequent (Poor and Smith 2004). The individual travel cost (ITC) is a more sophisticated model and uses trips per year (or season) by individual users of a site as the dependent variable. Overall, ITC provides more precise results. The ITC uses survey data from individual visitors to link the demand for tourism to its determinants. These include location of the visitor’s home, length of the trip, amount of time spent on site, traveling and on-site expenses, visitors’ income or other information on the value of their time, perception of the bundle of characteristics of the destination and heritage site, as well as other socioeconomic characteristics (for a theoretical framework see, e.g., King and Mazzotta 2011; for an empirical analysis, Loomis et al. 2009 and Fonseca and Rebelo 2010). Alberini and Longo (2006), for example, study the value of conservation of cultural monuments in Armenia by estimating a travel cost model controlling for the site characteristics, price of the whole trip, income, and other individual visitor characteristics such as education, age, civil status, and respondent’s perceptions of the heritage sites.

Hence, based on the relevant literature on ITC, it is hypothesized that an individual i allocates his or her time and income for a bundle of nontradable goods and services in the marketplace, such as a visit to a museum. Hence, the relevant function used to predict visit frequency is the following:

Y_{i j} = f (X_{i j}, K_{i,} Z_{i}), i = 1, \dots, n,

where Y_ij is the number of visits undertaken to the site j; X_ij is the travel cost incurred in visiting the site, which includes variables such as distance from the place of habitual residence, accommodation costs, living costs (food, beverage, shopping, etc.); K_i are the socioeconomic characteristics of individual i (age, gender, number of family members, income); and Z_i is the individual’s perception of the bundle of characteristics of the destination and heritage site.

From an empirical perspective, it is important to identify the intrinsic characteristics of the dependent variable. In this case, as the objective is to predict repeat visitation to the museum, the dependent variable (expressed in terms of number of visits to the site) is a count variable. Hence, the dependent variable can take on only integer values and the distribution includes a Poisson and the negative binomial. The latter allows for the overdispersion that can occur if only a few individuals have a large number of visits, as this implies that the variance in visits is larger than the mean.

The methodological procedure used in this study consists of running an initial standard Poisson, where the distribution is given by

\begin{matrix} Prob (Y_{i} | w_{i}) = \frac{e^{- λ} λ^{y_{i}}}{y_{i}!} y_{i} = 0, 1, 2, \dots \\ E (y_{i} | x_{i}) = Var (y_{i} | x_{i}) = λ = e^{x_{i}^{’} β} \end{matrix}

The parameter λ represents the average and the variance, as assumed by the Poisson distribution, and is greater than zero. The variable w_i denotes the other controls such as socioeconomic characteristics of individual i (K_i), perception of the bundle of characteristics of the destination, and heritage site (z_i) and costs (x_i).

The Poisson model is nonlinear; however, it can be easily estimated by the maximum likelihood technique. In the literature, many extensions of the Poisson model are applied, depending on the characteristics of the empirical data and on the stringent condition of the mean equal to the variance as previously stated (Greene 2003).

Specifically, in this case, each call to the museum is at least one visit; that is, a record would not appear in the database if the respondent had not visited the Ötzi museum. The dependent variable assumes values that range from 1 (i.e., first time visit to the museum) to N. Thus, visit is zero- truncated, and a zero-truncated Poisson regression allows one to model visit with this specific restriction. This model is specified by the following equation:

Prob (Y_{i} = y_{i} | w_{i} > 0) = \frac{e^{- λ} λ^{y_{i}}}{y_{i}!} \frac{1}{(1 - e^{- λ})} y_{i} = 1, 2, \dots

Bolzano as a Cultural Destination

Bolzano is a city of approximately 104,000 inhabitants and the capital of the autonomous province of Trentino Alto Adige, situated in the northeast of Italy.

The economy is based on tourism, high-quality intensive agriculture (including wine, fruit, and dairy products), traditional handicrafts (wood, ceramics), and advanced services. Bolzano combines different cultures and blends Italian and North-European architectural features. Churches, palaces, castles, and museums are of high artistic value.

During the past two decades, the city’s cultural life has experienced a new impulse that has seen the opening of numerous museums as well as multiple summer and winter events, such as the “Christmas Markets.” The city has a diversified cultural offering, which ranges from eno-gastronomic activities in the valleys, to mountain holiday and well-known cultural events, such as the Südtirol Jazz Festival and the Bolzano Festival.

Bolzano also hosts many art galleries, for example, the Galleria Goethe, Galleria Civica, and Galleria Les Chances de l’Art, and opened the first museum of the entire region, the Civic Museum of Bolzano, in 1905. A number of other museums have been opened in the past two decades, among which the Museion, a modern and contemporary art museum, and a museum center for the Messner Mountain Museum project. Referring to the latter and to the South Tyrol’s Museum of Archaeology, the Sunday Times (2006) described Bolzano as the “world’s center of mountain history and achievement.” This growing trend reflects the city’s increasing attention toward arts and culture.

The Archaeological Ötzi museum, opened in March 1998, hosts the world’s best-known and most well preserved human mummy, Ötzi the Iceman. An intact body from the Copper Age, along with his clothing and equipment, was accidently discovered in 1991 in the Ötzal Alps where it had lain preserved in ice for more than 5,000 years. This extraordinary find, unique in the world, has attracted researchers from around the world and has become the main cultural magnet of the city of Bolzano.

The museum is spread over approximately 1,200 m² and the entire first floor is dedicated to the Iceman findings. It has a permanent exhibition on Alto Adige’s prehistory and history, and also hosts temporary exhibitions. Since its opening, it has had approximately 250,000 visitors per year.

From a theoretical perspective, the literature (Evans 2005), reports three models where cultural activity is included into the urban regeneration process: the culture-led regeneration model, where cultural activity has a high public profile and is frequently cited as a symbol of regeneration; the cultural regeneration model, where culture is fully integrated into an ad hoc strategy along with other activities in the environmental, social, and economic fields (see, e.g., Balibrea 2001); and, finally, the cultural and regeneration model, where cultural activity is not strategically integrated and both planning and intervention are of a small scale. The city of Bolzano can be re-induced into the latter framework.

Nowadays, the city of Bolzano offers a best-practice example of a cultural city. This is also confirmed by economic and environmental indicators that rank Bolzano as the Italian city with the highest standard of quality of life (Sole 24 Ore 2010). The province of Bolzano also ranks first in terms of economic freedom, which is estimated taking into account 38 indicators of its overall performance. Bolzano is the richest province in Italy in terms of per capita GDP, with more than 36,000 euros per resident, 2.6 times higher than that of the poorest Italian province, Crotone. Besides, the city of Bolzano is characterized by a rate of poverty of 4% for the total population of the province (in the South of Italy, this figure can reach 40%). It ranks second in terms of the unemployment rate—2.8% against the Italian average of 7.7%—and has a relatively high female employment rate of more than 50%. The average public expenditure on services is 417 euros, against a national average of 91 euros per capita (NuovaCosenza 2011)

Overall, Bolzano can be regarded as a province of excellence, characterized by a high performance in terms of business, jobs, public security, environment, health, and well-being. These outstanding figures provide further evidence that Bolzano is actually following a sustainable path of growth within a culture and regeneration framework.

Bolzano’s potential as an urban tourism destination and its relationship with the Ötzi museum can be better understood by comparing flows of tourist arrivals to the city with visitor flows to the museum. To this aim, data collected from the Statistical Office of the Province of Bolzano and museum visitors’ data (provided by the museum administration), for the time span 2007-2010, are used. Figure 1 depicts a comparison of these two seasonally adjusted time series.

Figure 1.

Comparison of tourism arrivals in Bolzano and museum visitors (January 2007 to December 2010)

The Pearson’s correlation coefficient is equal to +0.19683. This outcome suggests that museum visitors mostly spend their holidays in the province rather than in the city. As a matter of fact, the percentage of respondents who fit the definition of “tourists,” that is, who spend at least one night in South Tyrol, is 81%.

Empirical Analysis

The Questionnaire and the Sample

The survey was administered at the Ötzi museum in Bolzano from June to August 2010 via face-to-face interviews. Respondents were selected with a quota random sampling procedure based on age and gender, to capture heterogeneous demographic features. Based on the visitors’ data of the previous year, provided by the administrative office of the museum, the sample size was determined according to a 95% confidence level, with a 5% error. By the end of the survey, 724 complete interviews were successfully concluded.

The questionnaire contained 36 questions in total organized in four blocks: the first section asked information on the journey, the next gathered information about the city of Bolzano, then third on the visit to the museum and, finally, in the last section, a series of questions on the socioeconomic characteristics of the visitor. A Likert-type scale was used in the questions on the importance of visiting Bolzano and the museum, on the importance of information, motivation, satisfaction, and loyalty ranging from negative to positive. From not important to very important for the motivation factors, from strongly in disagreement to strongly in agreement for assessing tourist’s satisfaction, and from very unlikely to very likely for the loyalty factors.

The main characteristics of the sample are analyzed in order to give a better picture on visitors’ characteristics and expenditure pattern (Table 1). Most of the visitors (69%) came from European countries other than Italy. They are mostly male (55%), generally married or de facto (80%), with a family of three to four members (51%). Respondents between 41 and 55 years old are more interested in visiting the museum (52%) than other age ranges. Forty-nine percent were educated at the college level or higher. As far as income is concerned, 40% of the sample had a middle to high average income, while just 3% earn 20,000 euros per year or less.

Table 1.

Sample Characteristics

Residence (%)		Age (% in Category)
Bordering region	10	>55	17
Trentino Alto Adige	2	41-55	52
Rest of Italy	18	26-40	25
Europe	60	9-25	6
Others	10	Mean age (years)	45
Civil status (%)		Number of family components (%)
Single/never married	14	1-2	36
Married or de facto	80	3-4	51
Separate/divorced	4	5	10
Widow	2	>5	3
Income (% in category)		Education (%)
<€20,000	3	Below high school	19
€20,000-€40,000	22	High school	32
€40,000-€70,000	40	College degree or more	49
€70,000-€100,000	17
>€100,000	18
First visit in Bolzano (% yes)	58	First visit to the Archaeological Museum (% yes)	90
Visit Bolzano without Iceman (% yes)	62	Visit other city with Iceman (% yes)	63
Strong intention to return to Bolzanonext year (% yes)	24	Strong intention to return to the Iceman Museum next year (% yes)	11
Strongly recommend Bolzano(% yes)	40	Strongly recommend Iceman Museum (% yes)	56

Source: Our elaboration on sample data.

It is important to note that for 58% of the sample, this was their first visit to Bolzano and for 90% it was their first visit to the Archaeological Museum. The majority (62%) stated that they would visit the city even if it were not hosting Ötzi; however, the mummy has an enormous potential for tourist attraction considering that 63% would be willing to visit a different city if Ötzi was hosted there. Also, 11% expressed a strong intention to revisit the museum the following year, while 24% had a strong intention to come back to Bolzano. In addition, 40% would strongly recommend the city to relatives and friends, and 56% declared that they are very likely to recommend a visit to this museum to their relatives and friends.

Of the sample, 95% spent at least one night outside the habitual place of residence and therefore can be identified as tourists. Considering a family unit that spends at least one night out, the average expenditure for accommodation is approximately 96 euros per night and 61 euros for food and beverage. On balance, these visitors have a higher spending propensity in the museum shop, as well as in the city, than daily visitors (see Table 2). The descriptive statistics provide an insight into the attractiveness of this outstanding archaeological find and into the role of the museum in the urban context.

Table 2.

Expenditure (in euros) Pattern of Ötzi Visitors

Expenses Categories	Tourists	Day-Visitors
Food and beverage(per family unit)	61.52	48.33
Museum shop	14.46	12.98
Shopping in town	69.16	48.14
Overnight stay(per family unit)	96.43	–

Source: Our elaboration on sample data.

Econometric Results and Discussion

The parametric estimation is based on the theoretical framework previously specified. The relevant variables included into the model, and obtained by the survey data, are described in greater details in Table 1A (Appendix).

The travel cost model is estimated by using Stata 10 and results are reported on coefficients and the incidence rate ratio (IRR) obtained by exponentiating the Poisson regression coefficient (Table 3). The best specification has been identified as a zero-truncated Poisson, since the dependent variable, number of visits to Ötzi, allows for the specific restriction, ranging from 1 to N (i.e., the count variable cannot be zero). Applying the goodness-of-fit test in the standard Poisson model, the null hypothesis (i.e., the empirical model fits the data) cannot be rejected (i.e., goodness-of-fit χ² = 74.72 – Prob > χ² (634) = 1.0000). Comparing the standard Poisson with the zero truncated Poisson specification, the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) criteria are minimized in the latter model. Besides, the Wald test indicates that the overall model is well specified at the level of significance of 1%. Hence, there is statistical ground to retain the zero-truncated Poisson as a better empirical specification. The model has been estimated correcting for robust standard errors, considering the relatively low number of observations that may lead to heteroskedasticity problems in the residuals.

Table 3.

Zero-Truncated Poisson Regression Results

Variables	Coefficients	IRR^a
Nationality (reference group foreigners)	−0.3492 (0.2697)	0.7052 (0.1902)
Age	0.0157 (0.0150)	1.0158 (0.0152)
Gender (ref. female)	0.4635** (0.2292)	1.5897** (0.3644)
Education	0.0357 (0.0716)	1.0363 (0.0742)
Employment (reference group employment2: full-time or part-time employee)
Employment1	−0.00275 (0.3573)	0.9974 (0.3564)
Employment3	−0.7446 (0.6220)	0.4748 (0.2954)
Employment4	−10.6561*** (0.7016)	0.0000*** (0.0000)
Employment5	−1.1533* (0.6985)	0.3155* (0.2204)
Employment6	0.3282 (0.7500)	1.3885 (1.0415)
Employment7	−0.7270 (0.4828)	0.4833 (0.2333)
Civil status (reference group status2: married or de facto)
Status1	−0.0935 (0.4111)	0.9107 (0.3744)
Status3	−0.2550 (0.3902)	0.7748 (0.3023)
Status4	−0.2963 (0.6689)	0.7435 (0.4973)
Number of family components	0.0353 (0.0275)	1.0360 (0.0285)
Number of people in the family receiving an income	−0.3280** (0.1477)	0.7203** (0.1064)
Income (reference group income3: from €40,000 to €70,000)
Income1	−0.9155 (0.5907)	0.4003 (0.2364)
Income2	−0.0086 (0.4029)	0.9913 (0.3995)
Income4	0.1837 (0.3207)	1.2016 (0.3854)
Income5	0.2661 (0.4024)	1.3049 (0.5251)
Travel cost	0.0025*** (0.0005)	1.0025*** (0.0006)
Total accommodation costs	−0.0000 (0.0001)	0.9999 (0.0001)
Total food and beverage costs	0.0006** (0.0002)	1.0006** (0.0002)
Shopping expenditure in Bolzano	0.0031* (0.0018)	1.0031* (0.0018)
Souvenir expenditure at Otzi	0.0136 (0.0149)	1.0137 (0.0151)
Price	−0.2320** (0.2191)	0.7929** (0.1737)
Time spent visiting Otzi	0.0041 (0.0021)	1.0041 (0.0021)
Bad weather	0.1193 (0.1062)	1.1267 (0.1197)
Relax	0.0492 (0.0774)	1.050 (0.0814)
Learn archaeology of South Tyrol	−0.0290 (0.1013)	0.9713 (0.0983)
Something different to do	−0.0687 (0.1207)	0.9335 (0.1127)
Nothing to do	−0.0994 (0.1460)	0.9053 (0.1322)
Importance to visit friends and family	0.1188 (0.0982)	1.1261 (0.1106)
Advised	−0.0913 (0.1021)	0.9126 (0.0931)
Curiosity	−0.2081** (0.0836)	0.8120** (0.0679)
Work or study visit	0.0281 (0.1345)	1.0281 (0.1383)
Visit Otzi in next five years	0.3450*** (0.0872)	1.4120*** (0.1232)
Suggest to visit Otzi	−0.2190** (0.0907)	0.8032** (0.0728)
Importance to visit Otzi	−0.0405 (0.0996)	0.9602 (0.0957)
Importance to visit other museums	0.1708* (0.0941)	1.1862* (0.1117)
Number of times in Bolzano	0.5139*** (0.0538)	1.6719*** (0.0900)
Constant	−1.8423 (1.6792)
Number of observations	675	675
Pseudo-R²	0.5906	0.5906
Wald χ²(40)	1294.53	1294.53
	Prob > χ² = 0.000	Prob > χ² = 0.000
Log pseudo-likelihood	−204.08	−204.08
AIC	490.16	490.16
BIC	675.27	675.27
Goodness-of-fit test	χ² = 74.72	Prob > χ²(634) = 1.000

Note: Robust standard errors are in parentheses. AIC = Akaike information criterion; BIC = Bayesian information criterion.

The incidence rate ratios (IRRs) indicate the exponentiated coefficients= e^b.

***

p < .001, **p < .05, *p < .1.

Among the sociodemographic characteristics, ceteris paribus it emerges that female visitors have a significant expectation to repeat visit of 1.58 times. Comparing employment status, the unemployed (with a very high statistically significant coefficient) and retired are less likely to revisit the museum compared with employed respondents. Moreover, as the number of family members receiving an income increases, the propensity to visit the museum decreases, possibly validating the hypothesis that families with youngsters are more interested in cultural activities.

Travel costs (with a highly statistically significant coefficient), food and beverage expenses, and spending for shopping in Bolzano have a rather marginal effect on the repeat visitation rate, though with positive coefficients. Nevertheless, entry fees negatively affect the probability to return, discouraging future visits. Several studies have examined the pricing rules for entry fees. For example, Lampi and Orth (2009) assess the effect of the introduction of an entrance fee on the Museum of World Culture in Sweden. They show that visitors who regularly visit cultural attractions are willing to visit the museum regardless of the fee level. However, four of the six target groups declare to be less likely to visit the museum after the implementation of the fee. They show that the composition of museum visitors, which was not evenly distributed across different socioeconomic groups during the free entrance period, became even more skewed after the introduction of the fee. Hence, the findings in the present study further confirm that the actual entrance fee at Ötzi discourages future repeat visits, possibly more among unemployed and retired visitors with a lower level of income. As a matter of fact, Bolzano also has the highest consumer price index in Italy.

A set of further controls highlight how pull forces may encourage a revisit to the museum in the future. The findings reveal that overall, except for “curiosity” (that shows a coefficient with a negative sign), these factors do not have any statistical effects on repeat visits. Furthermore, the higher the probability to return to Ötzi in the next five years, the higher the expected number of visits. Also, the presence of other cultural sites in Bolzano has a positive and statistically significant influence on the possibility of a repeat visitation to Ötzi as well as on the number of times respondents choose the city as their destination. Nevertheless, the probability to recommend the site to friends and relatives reduces the level of expected revisit by the respondent.

From these empirical findings, important implications arise from a marketing and management point of view. First, the results indirectly indicate that the museum has a special role for education, as families with youngsters are more likely to revisit. Hence, an ad hoc marketing policy may be directed to Italian and German schools, Bolzano being a bilingual city. Ideally, the museum would benefit from more interactive teaching labs designed for children and students of different ages. Digital access centers would allow visitors to experience a new dimension of learning. For instance, an interactive prehistorical overview of comparable sites at the time Ötzi lived would increase the educational value of the visit.

Second, the travel cost and, in general, living costs at the destination and site show positive coefficients, which imply that visitors are willing to travel a longer distance in order to visit the museum. This is consistent with the idea that visiting a cultural attraction may be regarded as a form of activity that helps the visitor to escape from his or her daily routine.

Third, the importance to visit other museums as a driver for repeat visits can be used by museum managers in a more efficient manner. As reported in Aalst and Boogaarts (2004), for Amsterdam’s Museumplein and Berlin’s Museuminsel cases, cooperation among local museums may also be successful for the Province of Bolzano. A best practice solution may involve the development of same-style brochures that advertise each other through complementary offers, together with a common website shared by all museums. The visitor may purchase a pass that encourages multiple visits within a museum network.

Discussion and Conclusions

This paper has contributed to assess the role of a museum as an attraction for urban tourism, examining what factors influence the intention to repeat the visit. The case study is the South Tyrol’s Museum of Archaeology in Bolzano (Italy), best known as the Ötzi museum. It is well established that cultural and heritage facilities require means to increase attendance and self-generated revenues. Hence, heritage administrators should pay particular attention to, and focus on, customer service, partnerships and network opportunities (Silberberg 1995). However, emulating a successful city case will not automatically create other attractive places. A fundamental feature that acts as a pull factor for a city is the uniqueness of its urban attraction (Park 2005). In this respect, the Iceman Ötzi, in the city of Bolzano, is a unique find of human history, a link between past and present that represents a source of knowledge and education for a vast audience as well as a potential urban flagship. Hence, the investigation on the degree of attractiveness of this outstanding cultural site can be regarded as a good economic indicator for enhancing urban tourism.

On this basis, it seemed of interest to analyze visitors’ experience and their intention to revisit the museum, and the city of Bolzano, in the future. To this aim, a travel cost model has been used that has the advantage to estimate economic values based on market prices as well as on what people do, rather than on what people would do in a hypothetical situation, as in stated preferences methods (Ha 2007). Empirically, given the specific characteristics of the dependent variable (i.e., number of visits to the museum), a zero-truncated Poisson has been estimated using survey data collected from June to August 2010.

Knowing consumers’ characteristics, motivations, and preferences is of high value in determining the promotion, position, and pricing of the cultural attraction as well as in deciding whether to invest in the tourism activity. On the whole, the city of Bolzano results were framed within a cultural and regeneration model—where cultural activity does not seem to be strategically integrated within a broader urban tourism image—in the South Tyrol province. There is empirical ground to believe that the planning and intervention implemented so far are still small-scale and need to be further challenged with an ad hoc marketing policy. In this respect, knowledge of the human past is a key factor that may be strategically exploited in order to increase the attractiveness of the city. To enhance urban attractiveness in the future, dedicating a new building to Ötzi may be regarded as an economic investment, since the actual building is widely held to be inadequate in size, a fact recognized by the museum’s managers. International archaeological and anthropological researchers are interested in studying this ancient and unique mummy: a more spacious infrastructure could provide a venue for international conferences, research, and a vital cultural attraction for the city of Bolzano.

The present paper has contributed to the museum literature by implementing a more sophisticated econometric tool to assess the degree of urban attractiveness of a cultural city. Nevertheless, a further strand of research, based on the present study, could be aimed at investigating the residents’ willingness to pay to invest in a restyled destination, having Ötzi as one of the main urban icons. It would also be of interest to apply the current investigation to other cultural sites in order to find common features.

Footnotes

Appendix

Table 1A.

List of Control Variables

Name	Definition
Nationality (reference group Foreigners)	This dummy takes the value one if the visitor is foreigner, zero otherwise.
AGE	Age of the respondent
GEN (reference group male)	This dichotomous variable takes the value one if female, zero if male.
Education	This is a discrete variable that takes the value one for the lowest level of education (i.e., primary school) and up to 7 for the highest level of education (i.e., PhD).
Employment (reference group empl2: full-time or part-time employee)	Employment1: autonomous; Employment3: working occasionally; Employment4: unemployed; Employment5: retired; Employment6: student, Employment7: housewife.
Civil status (reference group status2: married or de facto)	Status1: Single/never married; Status3: Separate/divorced; Status4: Widow.
Number of family components	This discrete variable takes into account the size of the family of the respondent.
Number of people in the family receiving an income	This discrete variable takes into account how many people of the family are receiving an income.
Income (reference group income3: from €40,000 to €70,000)	Income1: up to €20,000; Income2: from €20,000 to €40,000; Income4: from €70,000 to €100,000; Income5: more than €100,000.
Travel cost	This is a continuous variable that accounts for travel expenses and has been calculated as (2 × cost of single travel)/(n_people × n_days)
Total accommodation costs	This is a continuous variable that accounts for total accommodation costs, expressed in euros, undertaken by the respondent in all official (i.e., hotel, nonhotel—camp sites, agrotourism, serviced apartments) and nonofficial tourism infrastructure such as second homes and friends and family.
Total food and beverage costs	This is a continuous variable that accounts for the expenditures, expressed in euros, undertaken by the respondent to purchase food and beverage.
Shopping expenditure in Bolzano	This is a continuous variable that accounts for the shopping expenditure, expressed in euros, undertaken by the respondent.
Souvenir expenditure at Otzi	This is a continuous variable that accounts for the costs, expressed in euros, undertaken by the respondent to purchase goods at the Archaeological Museum.
Entry fees	This is a continuous variable that accounts for the ticket expenses to get to the Archaeological Museum
Time spent visiting Otzi	This is a discrete variable that accounts for the time (i.e., minutes) the respondent spent in the whole visit.
Bad weather	This is a discrete variable that takes values from 1 (not important at all) up to 5 (very important) for attributing an increasing importance for visiting the Archaeological Museum during bad weather conditions.
Relax	This is a discrete variable that takes values from 1 (not important at all) up to 5 (very important) for attributing an increasing importance to relaxation during the visit to the Archaeological Museum.
Learn archaeology of South Tyrol	This is a discrete variable that takes values from 1 (not important at all) up to 5 (very important) for attributing an increasing importance to Learn archaeology of South Tyrol during the visit to the Archaeological Museum.
Something different	This is a discrete variable that takes values from 1 (not important at all) up to 5 (very important) for attributing an increasing importance to do something different corresponding to Archaeological Museum visitation.
Nothing to do	This is a discrete variable that takes values from 1 (not important at all) up to 5 (very important) for attributing an increasing importance for visiting the museum, given the respondent has anything else to do.
Importance to visit friends and family	This is a discrete variable that takes values from 1 (not important at all) up to 5 (very important) for attributing an increasing importance for visiting the museum, given the respondent is visiting friends and family.
Advised	This is a discrete variable that takes values from 1 (not important at all) up to 5 (very important) for attributing an increasing importance for visiting the museum, given the respondent was advised to do so.
Curiosity	This is a discrete variable that takes values from 1 (not important at all) up to 5 (very important) for attributing an increasing importance for visiting the museum, given the respondent was curious.
Work or study visit	This is a discrete variable that takes values from 1 (not important at all) up to 5 (very important) for attributing an increasing importance for visiting the museum, given the respondent was doing a part of his or her job or a study visit.
Visit Otzi in next five years	This is a discrete variable that takes values from 1 (very unlikely) up to 5 (very likely) for the possibility the respondent returns in the next five years.
Suggest to visit Otzi	This is a discrete variable that takes values from 1 (very unlikely) up to 5 (very likely) for the possibility the respondent recommends the Archaeological Museum to friends and family.
Importance to visit Otzi	This is a discrete variable that takes values from 1 (not important at all) up to 5 (very important) for attributing an increasing importance for visiting the city of Bolzano, given the presence of the Archaeological Museum.
Importance to visit other museums	This is a discrete variable that takes values from 1 (not important at all) up to 5 (very important) for attributing an increasing importance for visiting the city of Bolzano, given the presence of museums other than the Archeological.
Number of times in Bolzano	This discrete variable takes into account the number of times the respondent has been in Bolzano.

Acknowledgements

The authors acknowledge the insightful comments provided by the anonymous referees, although the views expressed here are those of the authors.

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) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the Autonomous Province of Bolzano, project “Le attrazioni culturali e naturali come motore dello sviluppo turistico. Un’analisi del loro impatto economico, sociale e culturale” and by the Free University of Bolzano, project “Heritage, Sustainability and Economic Growth”.

Bios

Juan Gabriel Brida is Associate Professor of Economics at the School of Economics and Management, Free University of Bolzano. His research interests and expertise are in the areas of tourism economics and economic growth. He has a degree in Mathematics from the Universidad de la Republica (Uruguay) and a PhD in Economics from the University of Siena.

Marta Meleddu is a Research Fellow at the Economics Department (DiSEA), University of Sassari & CRENoS, Via Torre Tonda, 34, 07100 Sassari, Italy. She holds a Master in Economics and Econometrics at the Univerity of Bristol (UK) and a PhD at the University of Sassari. Her research interests include: cultural economics, tourism economics, environmental economics, economics of crime and consumer choice.

Manuela Pulina is a Lecturer at the Economics Department (DiSEA), University of Sassari & CRENoS, Via Torre Tonda, 34, 07100 Sassari, Italy. She holds a PhD at the University of Southampton (UK). Her main research interests include: tourism economics, crime economics, growth and consumer behaviour.

References

Aalst Van

Irina

Boogaarts

Inez

. (2004). “From Museum to Mass Entertainment. The Evolution of the Role of Museums in Cities: Repeat Visitation in Mature Sun and Sand Holiday Destinations.” European Urban and Regional Studies, 9 (3): 195-209.

Alberini

Anna

Longo

Alberto

. (2006). “Combining the Travel Cost and Contingent Behavior Methods to Value Cultural Heritage Sites: Evidence from Armenia.” Journal of Cultural Economics, 30: 287-304.

Alcaraz

Celeste

Hume

Margee

Mort

Gillian S.

(2009). “Creating Sustainable Practise in a Museum Context: Adopting Service-Centricity in Non-profit Museums.” Australasian Marketing Journal, 17: 219-25.

Ashworth

Gregory

Page

Stephen J.

(2011). “Urban Tourism Research: Recent Progress and Current Paradoxes.” Tourism Management, 32: 1-15.

Balibrea

Mari P.

(2001). “Urbanism, Culture and the Post-industrial City: Challenging the ‘Barcelona Model.’” Journal of Spanish Cultural Studies, 2 (2): 187-210.

Bedate

Ana M.

Herrero

Luis C.

Sanz

Jose A.

(2004). “Economic Valuation of the Cultural Heritage: Application to Four Case Studies in Spain.” Journal of Cultural Heritage, 5: 101-11.

Bedate

Ana M.

Herrero

Luis C.

Sanz

Jose A.

(2009). “Economic Valuation of a Contemporary Art Museum: Correction of Hypothetical Bias Using a Certainty Question.” Journal of Cultural Economics, 33: 185-99.

Bonn

Mark A.

Sacha

Joseph-Mathews M.

Dai

Hayes

Steve

Cave

Jenny

. (2008). “Heritage/Cultural Attraction Atmospherics: Creating the Right Environment for the Heritage/Cultural Visitor.” Journal of Travel Research, 45: 345-54.

Boter

Jaap

Rouwendal

Jan

Wedel

Michel

. (2005). “Employing Travel Time to Compare the Value of Competing Cultural Organizations.” Journal of Cultural Economics, 29: 19-33.

10.

Burton

Christine

Louviere

Jordan

Young

Loise

. (2009). “Retaining the Visitor, Enhancing the Experience: Identifying Attributes of Choice in Repeat Museum Visitation.” International Journal of Nonprofit and Voluntary Sector Marketing, 14: 21-34.

11.

Çela

Ariana C.

Lankford

Sam

Knowles-Lankford

Jill

. (2009). “Visitor Spending and Economic Impacts of Heritage Tourism: A Case Study of the Silos and Smokestacks National Heritage Area.” Journal of Heritage Tourism, 43: 245-56.

12.

Choi

Andy S.

Ritchie

Brent W.

Papandrea

Franco

Bennett

Jeff

. (2010). “Economic Valuation of Cultural Heritage Sites: A Choice Modeling Approach.” Tourism Management, 31 (2): 213-20.

13.

Colombino

Ugo

Nene

Annamaria

. (2009). “Preference Heterogeneity in Relation to Museum.” Tourism Economics, 15 (2): 381-95.

14.

De Rojas

Carmen

Camarero

Carmen

. (2008). “Visitors’ Experience, Mood and Satisfaction in a Heritage Context: Evidence from an Interpretation Centre.” Tourism Management, 29: 525-37.

15.

Dunlop

Stewart

Galloway

Susan

Hamilton

Christine

Scullion

Adrienne

. (2004). “The Economic Impact of the Cultural Sector in Scotland.” http://www.christinehamiltonconsulting.com/documents/Economic%20Impact%20Report.pdf (accessed December 9, 2010).

16.

Edwards

Deborah

Griffin

Tony

Hayllar

Bruce

. (2008). “Urban Tourism Research Developing an Agenda.” Annals of Tourism Research, 35: 1032-52.

17.

Europa Inform. (2004). “The Economic Impact of Historical Cultural Tourism—Romit Project—Roman Itineraries.” http://www.romit.org/en/pubblicazioni.htm (accessed March 4, 2011).

18.

European Commission. (2006). “Tourism.” http://ec.europa.eu/enterprise/sectors/tourism/sustainability-competitiveness/index_en.htm (accessed April 6, 2011).

19.

Evans

Graeme

. (2005). “Measure for Measure: Evaluating the Evidence of Culture’s Contribution to Regeneration.” Urban Studies, 42: 959-83.

20.

Evans

Graeme

Shaw

Phyllida

. (2004). “The Contribution of Culture to Regeneration in the UK: A Review of Evidence.” A Report to the Department of Culture Media and Sport. London: London Metropolitan University.

21.

Fonseca

Susana

Rebelo

Joao

. (2010). “Economic Valuation of Cultural Heritage: Application to a Museum Located in the Alto Douro Wine Region—World Heritage Site.” Pasos, 8 (2): 339-50.

22.

Frey

Bruno S.

Meier

Stephan

. (2006). “The Economics of Museums.” In: Handbook of the Economics of Art and Culture, Vol. 1, edited by Ginsburgh

Victor A.

Throsby

David

. Amsterdam: Elsevier.

23.

Greene

William

2003. Econometric Analysis. 5th edition. Upper Saddle River, NJ: Prentice Hall.

24.

Hamnett

Chris

Shoval

Noam

. (2003). “Museums as ‘Flagships’ of Urban Development.” In: Cities and Visitors: Regulating People, Markets, and City Space, edited by Hoffman

L. M.

Judd

Fainstein

S. S.

Oxford, UK: Blackwell.

25.

Hotelling

Harold

. (1949). Letter to the National Park Services: An Economic Study of the Monetary Evaluation of Recreation in the National Parks. Washington, DC: U.S. Department of the Interior, National Park Service/Recreational Planning Division.

26.

Hume

Margee

. (2011). “How Do We Keep Them Coming? Examining Museum Experiences Using a Services Marketing Paradigm.” International Journal of Nonprofit and Voluntary Sector Marketing, 23 (1): 71-94.

27.

Martinez-Espiñeira

Roberto

Loomis

John B

Amoako-Tuffour

Joe

Hilbe

Joseph M.

(2008). “Comparing Recreation Benefits from On-Site versus Household Surveys in Count Data Travel Cost Demand Models with Overdispersion.” Tourism Economics, 14 (3): 567-76.

28.

Law

Cristopher M.

(ed.). (1996). Urban Tourism: Attracting Visitors to Large Cities. London: Mansell.

29.

Loomis

John

Tadjion

Omer

Watson

Philip

Wilson

Josh

Davies

Stephan

Thilmany

Dawn

. (2009). “A Hybrid Individual Zonal Travel Cost Model for Estimating the Consumer Surplus of Golfing in Colorado.” Journal of Sports Economics, 10 (2): 155-67.

30.

Mazzanti

Massimiliano

. (2003). “Discrete Choice Models and Valuation Experiment.” Journal of Economic Studies, 30 (6): 584-604.

31.

Gil

Sergio M.

Ritchie

Brent R. B.

(2009). “Understanding the Museum Image Formation Process: A Comparison of Residents and Tourists.” Journal of Travel Research, 47 (4): 480-93.

32.

Inhyuck “Steve”

. (2007). “Recreational Demand for a Gulf Coast Tourism Destination.” Journal of Economics and Economic Education Research, 8 (1): 51-63.

33.

Harrison

Paul

Shaw

Robert

. (2004). “Consumer Satisfaction and Post-purchase Intentions: An Exploratory Study of Museum Visitors.” Journal of Arts Management, 62: 23-32.

34.

Hellström

Jörgen

Nordström

Jonas

. (2008). “A Count Data Model with Endogenous Household Specific Censoring: The Number of Nights to Stay.” Empirical Economics, 35: 179-92.

35.

Jansen-Verbeke

Myriam

(1988). Leisure, recreation, and tourism in inner cities: Explorative case-studies. Amsterdam and Nijmegen: Koninklijk Nederlands Aardrijkskundig Genootschap.

36.

Jansen-Verbeke

Myriam

van Rekom

Johan

. (1996). “Scanning Museum Visitors: Urban Tourism Marketing.” Annals of Tourism Research, 23: 364-75.

37.

Jeong

Jae-Hoon

Lee

Kyung-Hoon

. (2006). “The Physical Environment in Museums and Its Effects on Visitors’ Satisfaction.” Building and Environment, 41: 963-69.

38.

King

Dennis M.

Mazzotta

Marisa J.

(2011). “Ecosystem Valuation.” http://www.ecosystemvaluation.org/travel_costs.htm (accessed April 12, 2011).

39.

Kinghorn

Naomi

Willis

Ken

. (2007). “Estimating Visitor Preferences for Different Art Gallery Layouts Using a Choice Experiment.” Museum Management and Curatorship, 22 (1): 43-58.

40.

Kinghorn

Naomi

Willis

Ken

. (2008). “Measuring Museum Visitor Preferences Towards Opportunities for Developing Social Capital: An Application of a Choice Experiment to the Discovery Museum.” International Journal of Heritage Studies, 14 (6): 555-72.

41.

Lampi

Elina

Orth

Matilda

. (2009). “Who Visits the Museums? A Comparison between Stated Preferences and Observed Effects of Entrance Fees.” Kyklos, 62 (1): 85-102.

42.

Luksetich

William A.

Partridge

Mark

. (1997). “Demand Functions for Museum Services.” Applied Economics, 29 (12): 1553-9.

43.

Maddison

David

. (2004). “Causality and Museum Subsidies.” Journal of Cultural Economics, 28 (2): 89-108.

44.

Maddison

David

Foster

Terry

. (2003). “Valuing Congestion Costs in the British Museum.” Oxford Economic Papers, 55 (1): 173-90.

45.

Mourato

Susana

Mazzanti

Massimiliano

. (2002). Economic Valuation of Cultural Heritage: Evidence and Prospects in Assessing the Value of Cultural Heritage. Los Angeles: Getty Conservation Institute.

46.

NuovaCosenza. (2011). “Economia, Finanza e Sviluppo.” Quotidiano On-line. http://www.nuovacosenza.com/economia/index.html (accessed April 20, 2011).

47.

Packer

Jan

Bond

Nigel

. (2010). “Museums as Restorative Environments.” Curator 53 (4): 421-36.

48.

Park

Jungyoon

. (2005). “Role of the Culture and Tourism Development in the Strategic Planning for City Attractiveness.” http://aesop2005.scix.net/data/papers/att/241.fullTextPrint.pdf (accessed September 9, 2011).

49.

Paswan

Audhesh K.

Troy

Lisa A.

(2004). “Non-profit Organization and Membership Motivation: An Exploration in the Museum Industry.” Journal of Marketing Theory and Practice, 12 (2): 1-15.

50.

Plaza

Beatriz

. (2000). “Guggenheim Museum’s Effectiveness to Attract Tourism.” Annals of Tourism Research, 27 (4): 1055-8.

51.

Plaza

Beatriz

. (2008). “On Some Challenges and Conditions for the Guggenheim Museum Bilbao to Be an Effective Economic Re-activator.” International Journal of Urban and Regional Research, 32 (2): 506-17.

52.

Plaza

Beatriz

. (2010). “Valuing Museums as Economic Engines: Willingness to Pay or Discounting of Cash-Flows?” Journal of Cultural Heritage, 11: 155-62.

53.

Plaza

Beatriz

Haarich

Nilke S.

(2009). “Museums for Urban Regeneration? Exploring Conditions for Their Effectiveness.” Journal of Urban Regeneration and Renewal, 23: 259-71.

54.

Poor

Joan P.

Smith

Jamie M.

(2004). “Travel Cost Analysis of a Cultural Heritage Site: The Case of Historic St. Mary’s City of Maryland.” Journal of Cultural Economics, 28: 217-29.

55.

Rudan

Elena

. (2010). “The Development of Cultural Tourism in Small Historical Towns.” Tourism & Hospitality Management, 10: 577-86.

56.

Sanz

Jose A.

Herrero

Luis C.

Bedate

Ana M.

(2003). “Contingent Valuation and Semiparametric Methods: A Case Study of the National Museum of Sculpture in Valladolid, Spain.” Journal of Cultural Economics, 27: 241-57.

57.

Scarpa

Riccardo

Thiene

Mara

Tempesta

Tiziano

. (2007). “Latent Class Count Models of Total Visitation Demand: Days Out Hiking in the Eastern Alps.” Environmental Resource Economics, 33: 447-60.

58.

Scott

Carol

. (2006). “Museums: Impact and Value.” Cultural Trends, 15 (1): 45-75.

59.

Silberberg

Ted

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

60.

Smith

Andrew

Strand

Ingvild Von Krogh

. (2011). “Oslo’s New Opera House: Cultural Flagship, Regeneration Tool or Destination Icon?” European Urban and Regional Studies, 18 (1): 93-110.

61.

Sole 24 Ore. (2010). “Classifica Italia, vince Bolzano.” http://www.tgcom.mediaset.it/economia/articoli/articolo497525.shtml (accessed April 19, 2011).

62.

Stynes

Daniel J.

Vander Stoep

Gail A.

Sun

Ya-Yen

. 2004. “Estimating Economic Impacts of Michigan Museums.” Department of Park, Recreation and Tourism Resources, Michigan State University.

63.

The Sunday Times. (2006). “The Mountain Men of North Italy.” http://www.timesonline.co.uk/tol/travel/destinations/italy/article670226.ece (accessed March 15, 2011).

64.

Throsby

David

. (2001). Economics and Culture. Cambridge, UK: Cambridge University Press.

65.

Thyne

Maree

. (2001). “The Importance of Values Research for Nonprofit Organisations: The Motivation-Based Values of Museum Visitors.” International Journal of Nonprofit and Voluntary Sector Marketing, 62: 116-30.

66.

Ward

Frank A.

Loomis

John B.

(1986). “The Travel Cost Demand Model as an Environmental Policy Assessment Tool: A Review of Literature.” Western Journal of Agricultural Economics, 11 (2): 164-78.