scispace - formally typeset

Journal ArticleDOI

A Dynamic Analysis of Tourism Determinants in Sicily

01 Jun 2015-Tourism Economics (SAGE Publications)-Vol. 21, Iss: 3, pp 441-454

AbstractThis study provides an initial analysis of the key determinants of tourism in Sicily. In doing so, it responds to the general lack of a scientific approach in the study and management of tourism in Sicily. By mixing a gravity approach and system dynamics methodology, the attractiveness of Sicily is examined, taking into account both structural and promotional aspects that might affect tourism demand. The results strongly suggest that the island’s natural and cultural resources, the road infrastructure and the urban environment are important determinants of tourism demand in Sicily. The findings may be useful for local authorities involved in the development of tourism, and represent a starting point for further research dealing with future trends.

Topics: Tourism geography (67%), Tourism (63%)

Summary (12 min read)

Jump to: [1.1 History][1.2 Geography][1.3 Cultural resources][1.4 Natural endowments][1.5 Tourism infrastructures in Sicily][1.6 The most of tourism opportunities in Sicily][1.7 The competitors][1.8 Trends and the “11 September” effect for tourism in Sicily][1.9 Tourism market position of Sicily and more recent developments][1.10 The reputation of Sicily][2.1 The gravity model][2.1.1 A gravity formulation for human interactions][2.1.2 The gravity approach in tourism related studies][2.2 The system dynamics approach][2.3 The case study][2.3.1 International demand functions for tourism in Sicily][2.3.2 National demand function for tourism in Sicily][2.3.3 Local demand function for tourism in Sicily][2.3.4 Parameters’ estimation][3.1 The hotel sector][3.2 The restaurant sector][3.3 The culture sector][3.4 The nature sector][3.5 The urban environment sector][3.6 The road sector][3.7 The reputation sector][4.1 Philosophical and technical problems in model validation][4.2 Structure validity][4.2.1 The structure confirmation test][4.2.2 Parameter confirmation test][4.2.3 Direct extreme-condition tests][4.2.4 Dimensional consistency test][4.2.5 Structure oriented behavior tests][4.3 Behavior validity][4.4 Sensitivity Analysis][5.1 Simulation result in the hotel sector][5.3 Simulation result in the culture sector][5.4 Simulation result in the nature sector][5.5 Simulation result in the urban environment sector][5.6 Simulation result in the road sector][5.7 Simulation result in the reputation sector][5.8 French tourists][5.9 German tourists][5.10 Norwegian tourists][5.13 Rest-of-Italy tourists][5.14 Local tourists] and [5.15 Coefficients of the variables]

1.1 History

  • The magnificence of Sicily starts with the Greek domination.
  • When the two cultures began to clash, the Greek Punic Wars, the longest wars of antiquity, erupted.
  • In 535 AD, as the Roman Empire fell, Emperor Justinian I made Sicily a Byzantine province and, for the second time in Sicilian history, the Greek language became a familiar sound across the island.
  • A description of Palermo was given by Ibn Hawqal, an Arab merchant who visited Sicily in 950 AD.
  • The 1848 revolution was successful and resulted in a period of independence for 8 Sicily.

1.2 Geography

  • Located in the South of the country, just off the toe of the Italian peninsula, Sicily, with its total area of 25.711 square kilometers and a land area of 25.409 square kilometers1, is the most extended region in Italy and the widest island in the Mediterranean.
  • In fact, its winters are generally mild, with temperatures rarely dropping below 7-8°C, while the summers are long, hot and dry.
  • The main agricultural products are wheat, barley, corn, olives, citrus fruit, oranges, lemons, almonds, wine grapes, and cotton; cattle, mules, donkeys, and sheep are raised.
  • Sicily's manufactures include processed food, chemicals (in the area of Catania), refined petroleum, fertilizers, textiles, ships, leather 1 Both values are from EUROSTAT .

1.3 Cultural resources

  • Sicily still today shows traces of the many cultures that ruled over the centuries: the Greeks, Romans, Arabs, Normans, French and Spanish each made their mark, leaving important testimony of their presence through artistic and architectural works of the highest level.
  • A complete and updated list of antiquaria, archaeological sites and museums in Sicily can be downloaded from the web page of the Assessorato Beni Culturali, Ambientali e P.I. – Dipartimento Beni Culturali, Ambientali ed E. P. (in Italian).
  • Theatre complete the cultural supply of Sicily that allows the tourist also to experience a trip in the history in the range of several kilometers.

1.4 Natural endowments

  • If Sicily’s cultural endowment has no fear of comparison with the other Italian regions and main tourism competitors, the supply of a nature-based holidays in Sicily has to face the foreign and national competition instead.
  • Etna volcano, as angry and unreliable it may look with its constant bubbling and roaring activity, is always something to marvel at for its natural wonder.
  • An example of a unique and uncontaminated landscape is the archipelago of the Aeolian Islands (from Aeolus, the God of the winds of Greek mythology) composed of seven islands that form a pattern resembling the letter “y” along the north-eastern coast of Sicily.
  • 11 The pristine and splendid Sicilian sea with its 99,27% of bathing area (the Italian average being 92,00%), the very good locations for diving and the exploration of the remains of ancient crafts, the richly colourful underwater vegetation and a flourishing fauna protected by UNESCO complete the naturalistic panorama of Sicily.

1.5 Tourism infrastructures in Sicily

  • The accommodation structures in Sicily in 2007, hotels and complementary structures, account for 2,83% of the national total (3,44% hotels and 2,61% complementary structures8).
  • The most common type of structure, however, continues to be the three-star hotel (over 50%)10.
  • Among the extra-hotel structures, agritourism and B&Bs (bed and breakfast) are the types of accommodation showing the greatest recent growth.
  • Moreover, in the last years, a series of renovation projects able to combine the demand of new form of tourism with the necessity to preserve unique architectural features have made sleeping in castles, country homes, “bagli” (stone manors), villas, aristocratic residences, ancient convents, farms and farmhouses very common in Sicily.

1.6 The most of tourism opportunities in Sicily

  • The unique features of Sicily make possible different forms of tourism.
  • 11 XV Rapporto sul turismo italiano (fifteenth report on Italian tourism), Mercury (2006).
  • 12 second for its sea and the beauty of its coasts; third for its cuisine and wines; first in terms of the welcoming nature, culture and charm of its inhabitants; third for its local lifestyle; fourth for the affordability of a vacation13.
  • In the real context of the holiday market, however, the seaside tourism is actually prevailing.
  • The cuisine and wine related tourism is in a growing phase, experiencing a positive trend because of the increasing diffusion of “Wine Tours” and agriturisms, allowing visitors to eat typical dishes prepared with very genuine ingredients.

1.7 The competitors

  • Sardinia (32%), for the sea and beaches, and Greece (21%) for the combination art and sea represent the main competitors for Sicily.
  • Other tourism areas, like the French and the Dalmatian ones, do not exert a real competition against Sicily because of the characteristic of their supply more traditional and less integrated with the art and archaeology.
  • In spite of its wonderful natural resources Sicily has to leave the supremacy of nature and mountains to Trentino, Val d’Aosta, Lombardia, Tuscany and Calabria.

1.9 Tourism market position of Sicily and more recent developments

  • Sicily is the tenth Italian region (Italy is made of twenty regions) in terms of tourism presence, and the second in the Southern Italy.
  • Before Sicily, Veneto, Trentino Alto Adige, Tuscany, Emilia Romagna, Lombardia, Lazio, Campania, Liguria and Marche play the role of the lion in the national and international tourism market.
  • Even worse, using a territorial ratio (resident population/tourism presences), Sicily is the fifth from the last in comparison with the other Italian regions.
  • The tourism density per square meter (arrivals/Km2) in Sicily is below the national average (178 versus the national average of 247) and push Sicily in thirteenth position.
  • Over the last few years, in addition to the more traditional form of tourism that gravitates around Sicily, newer tourism products have been developed on the base of local customs, traditions, culture and sport, encouraging more and more tourists to return in Sicily in less crowded periods of the year.

1.10 The reputation of Sicily

  • There are those who are frightened by Sicily’s reputation for crime, and those who think it is fascinating or, indeed, glamorous.
  • Shrouded by secrecy, protected by blood-oaths, murders and bribery, the Mafia (or Cosa Nostra) has long exerted its hold over Sicily, particularly over Palermo and the western half of the island.
  • The last couple of decades, however, have seen an unprecedented openness, as Italy attempts to come to terms with its legacies of criminality and bloodshed.
  • In the latter context, methods focused on non-causal, mainly time-series modelling and on causal, mainly econometric techniques, represent the two alternative approaches.
  • More precisely, three gravity models are formalized to define the international, national and local demand for tourism in Sicily.

2.1 The gravity model

  • Gravity models take their name from the Newton's law of universal gravitation first formulated in Newton's work Philosophiae Naturalis Principia Mathematica17 (Mathematical Principles of Natural Philosophy).
  • Today, Newton's law of universal gravitation is a widely accepted theory.
  • It guides the efforts of scientists not only in physics but, duly rearranged, in fields like biology, medicine, transport engineering, social sciences and economics as well.
  • In the wake of such an empirical success, several authors have also provided an economic theoretical foundation of the gravity model (Bergstrand (1985); Deardorff (1998); Földvári (2006)).
  • The microeconomic approach, according to which the model of spatial interaction derives from the application of the theory of random utility to the choice of the localization.

2.1.1 A gravity formulation for human interactions

  • The gravity model of international trade was developed independently by Tinbergen (1962) and Poyhonen (1963).
  • In its basic form, the size of two countries is assumed to be measured by their national incomes whereas the distance between their economic centers works as a deterrence factor (see McCallum, 1995 and Boisso & Ferrantino, 1997).
  • Linnemann (1966), for instance, includes population as an additional measure of country size.
  • The bidirectional gravity model (2.2) can be easily extended to a unidirectional one by allowing the variables to have different parameters values (a vector of βs in the formula) for the origin and destination countries while using the same variables for both.
  • In this case measures the spatial interactions from i to j only and the equation’s terms in the right-hand side represents the attributes of the origin and the destination country separately.

2.2 The system dynamics approach

  • The use of System Dynamics in tourism analyses is relatively new.
  • Loutfi and Moscardini (2000) focused their analysis on the economic impact of tourism revenue on the 21 Egyptian economy using classical tourism multipliers.
  • They concluded about the opportunity to carry out tourism analyses mixing traditional econometric methods with System Dynamics models.
  • In 2001, Jambekar and Brokaw proposed a system dynamic structure for snowmobile tourism in the Keweenaw Peninsula in Michigan.
  • The proposed model lacked in the dynamic analysis of most of the tourism determinants.

2.3 The case study

  • Seven sectors create a structural network where the determinants of tourism flows result from the sectors’ specific features.
  • This leads to a rich data set which improves estimation accuracy and flexibility and is believed to yield more convincing results.
  • Real data, including historical Sicilian tourism flows from France, Germany, Norway, Spain and United Kingdom, have been used to calibrate the model.
  • Countries in the sample, selected in function of the different lifestyles, are modelled as tourism generating regions.
  • The period under study develops through nine years, since 1999 to 2007.

2.3.1 International demand functions for tourism in Sicily

  • The basic approach is to treat tourism flows as a demand system for differentiated products.
  • Time series provided by the Tourism Bureau of Sicilian Region have been used to calibrate the international tourism demand function.
  • For this reason the three other variables iStHTDIRECTFLIG , iStLOWCOST , and iStOSTDIRECTLOWC have been introduced in the model.

2.3.2 National demand function for tourism in Sicily

  • In the present study, the tourists’ flow in Sicily from the other Italian regions is also analysed.
  • Just a variable less, , makes the difference with the international function.
  • (2.5) 28 29 Variables keep the meaning shown in the previous paragraph with the only difference that the ubscript I stays for Italian regions, Sicily excluded.
  • Figure the national tourism demand inally, since local tourists compete with national and international tourists in the use of nts, km of sandy beaches, and so s ISDIST is calculated in kilometers between Rome and Palermo.
  • Since the log linear specification of (2.5) is very simil 2.2 shows the system dynamics model implementing function.

2.3.3 Local demand function for tourism in Sicily

  • F territorial resources (bed places in the hotels, seats in restaura on), a small demand function for regional tourism is also considered in the economic model.
  • = ), StSt ROADURBENV (2.6) In (2.6), lagged variables, population, tourism infrastructures (hotels, restaurants and roads), ultural and natural resources keep their important role in determining the local tourism demand c.
  • The double S in the subscript of the variables means that the origin and destination area coincide.
  • Figure 2.3 shows the system dynamics model implementing the local tourism demand function.

2.3.4 Parameters’ estimation

  • From an implementing point of view, a model formulation on the basis of continent-wise origins translates into an econometric model where each variable has its own elasticity parameter.
  • The real problem is that the panel data analysis yields a set of βs that summarizes the differences between the countries belonging to the panel (cross-section analysis) and the temporal effect (time series analysis).
  • For each optimization to run, variables have been gradually added in the formalization of the problem, in order to evaluate also the sensitivity of the specific sector to that variable, getting to the final parameter estimation with the maximum confidence in the values obtained.
  • In fact, each endogenous variable used in previously formalized tourism demand functions (international, national and local) has been inserted in a specific sector of the model in order to study its behavior over time.
  • To make all the sketches presented below more easily understandable, in addition to the normal stock and flow notation, the following standards have been applied: - The names of stock variables have been written with an initial capital letter; -.

3.1 The hotel sector

  • The local hotel sector presents two main characteristics.
  • The net utilization index (NUI), instead, takes the real number of days of activity in the year into account.
  • The hotel sector aims at describing and explaining such dynamics and their effect on the Sicilian tourism.
  • Then, the inflow of hotels acquisition and the outflow of hotels depreciation determine the value of the stock over time.
  • The bed-places acquisition is the first-order exponential smoothing39 of the variable gap in bedplaces capacity with a delay time equal to the AVERAGE TIME TO SET UP A NEW HOTEL Finally, the variable desired bed-places capacity keeps both the expected percentage of growth in tourism presences and the actual percentage of growth in tourism presences into 37 38.

3.2 The restaurant sector

  • The structure of the sector first makes a difference between Restaurants Inside the Tourism Area and Restaurants Outside the Tourism Area.
  • Residents eager to have a meal in restaurants inside the tourism area, local tourists and non-resident tourists therefore compete for a seat in the restaurants and together form the total demand for seats in restaurants inside the tourism area.
  • The supply of seats is strictly dependent of the rotation of seat, meaning the number of times the same seat is used during the restaurants’.
  • The average number of meals consumed in restaurants for non-travelling locals determines the number of seats for non-travelling locals looking for a seat in restaurants outside the tourism area that complete the set of variables defining the desired number of new restaurants outside the tourism area.

3.3 The culture sector

  • Culture is one of the main strengths for tourism supply in Sicily.
  • The Culture sector describes the evolution of such exploitation of the stock of Cultural Resources generated by the tourism pressure in Sicily in the period under study40.
  • Figure 3.3 shows the stock and flow structure of this model’s sector.
  • Related with the population of Sicily, this variable determines the tourism pressure, meaning the percentage of tourists with respect to the resident population.
  • The constant EFFECT OF SHORTFALL IN CULTURAL RESOURCES ON THE AVERAGE TIME TO MAKE ENJOYABLE NEW CULTURAL RESOURCES, indeed, settles the answer of the local public administration to the demand of new cultural resources.

3.4 The nature sector

  • The general attitude of tourists worldwide interested in an environmentally friendly holiday experience is becoming increasingly popular in Sicily.
  • Driven by the growing demand for a nature-based tourism, Sicily has sped up the process of acquisition of land and sea areas to its protected heritage41.
  • The structure of the nature sector is not explicitly showed here since it is very similar to the structure of the culture sector.
  • Therefore the stock of Natural Resources does not have any outflow from it.

3.5 The urban environment sector

  • The urban environment sector describes the quality of public areas, such as city centers, beaches, roads, and other places in which tourists experience their holidays.
  • The Road Sector participates in defining the road crowding with two variables: the density of vehicles in Sicily and the AVERAGE SUSTAINABLE NUMBER OF VEHICLES PER KM OF ROAD46.
  • // 44 45 The Waste Management Capacity multiplied by the usw production index yields the intervention for restoration and, as a consequence, the urban environment restoration, also known as http.
  • The variable desired wmc is the result of the pressure exerted on a greater management capacity both by the future forecast of usw production and the resources needed to restore the capacity saturated over time.

3.6 The road sector

  • Sicily, with its total area of 25,708 square kilometers, and a land area of 25,405 square kilometers51, is the largest region in Italy, and the largest island in the Mediterranean Basin.
  • The Road Sector is designed to study the evolution, over time, of the road network and its effect on the land accessibility in order to evaluate its contribution to the total attractiveness of Sicily as a tourism destination.
  • Roads is augmented by the flow road construction and decreased by the flow road disruption.
  • From northwest to southeast, and from northeast to southwest (and vice versa), allowing the accessibility of each vertex of the square from any other one and from any starting point on the diagonals.

3.7 The reputation sector

  • In international tourism, as in some other types of trade in services, the exporting country supplies itself and not only its products.
  • Therefore two stocks, Males in Sicily Aged 15 and Over with Primary or 55 The value of Mafia murders is strictly related to both the volume of forces (Police, Carabinieri and Guardia di Finanza) operating in Sicily against the Mafia, and the effectiveness of their activity.
  • Petty crime and Perception of Dangers Posed by the Mafia together define the total threat to the tourist in Sicily as the arithmetic mean of the two crimes whose contribution to the total threat is measured by the constants EFFECT OF PETTY CRIME ON TOTAL THREAT and EFFECT OF PERCEPTION OF DANGERS POSED BY THE MAFIA ON TOTAL THREAT, respectively.
  • The results of the validation process are reported along with the discussion of their significance for the model validity.

4.1 Philosophical and technical problems in model validation

  • The concept of model validity is tightly coupled with the reliability of the model with respect to the purpose for which it has been created.
  • Yet, in order to make the present discussion as rigorous as possible, just the formal aspects of model validity and model validation will be considered knowing already that judging the internal structure of a model is in the same way problematic.
  • The validity of the model presented in this study is therefore evaluated looking at the internal structure that adequately represents the aspects of the system relevant to the problem behavior in hand.
  • It is exactly in this perspective that validation tests have been carried out and are reported in the following sections.
  • Therefore, the validity of the model has been judged on the basis of its ability to explain the dynamics of tourism flows in Sicily as resulting from the evolution of local tourism supply in the time horizon considered: the “right behavior for the right reason”.

4.2 Structure validity

  • Structure validity results from carrying out two types of structure tests: Direct structure tests and Structure-oriented tests.
  • Direct structure tests aim at valuing the validity of the model structure by direct comparison between each relationship (a mathematical equation or any form of logical relationship) individually and the available knowledge obtained directly from the real system being modeled or from the existing literature.
  • In the first case the direct structure tests are classified as empirical, in the second case as formal.
  • The Structure-oriented confirmation tests, instead, assess the validity of the structure indirectly, by applying certain behavior tests on model-generated behavior patterns.
  • These tests involve simulations of the entire model and/or simulations of sub-models of it.

4.2.1 The structure confirmation test

  • This confirmation test characterizes the way the model has been created from the very beginning.
  • Indeed, the comparison between the equations of the model with the relationships that exist in the real system/literature is a process that starts with the definition of the first variable of the model and ends with the definition of last one.
  • 54 In the present study, the main source of information for defining the model equations has been the generalized knowledge in the literature.
  • In some cases, the relationships between the model’s variables have been formalized strictly following the conclusions of the statistical surveys used to drive the construction of some sector of the model (the relationship between criminality and the sex and age of criminals, for instance).

4.2.2 Parameter confirmation test

  • This crucial test is intended to compare the parameters used in the model with existing knowledge about the real world system.
  • For the model under analysis, the parameter confirmation comes directly from the common sense and the everyday life.
  • 55 A second category of model parameters deals with the smoothing factor for changes in information.
  • Finally, a third category of parameters concerns the initial value or the reference value for some model variables.
  • Concerning the issue of the sensitivity of the model to changes in parameters values, it has to be observed that some of the parameters can effectively influence the model behavior from a qualitative point of view.

4.2.3 Direct extreme-condition tests

  • According to this direct structure test, the validity of the model equations has to be assessed under extreme conditions.
  • More precisely, these tests aim at assessing the plausibility of the variables resulting value against the knowledge/anticipation of what would happen under similar conditions in real life.
  • This kind of tests have been run for all the variables of the model, although, in the process of definition of each model equation, some of the typical extreme analysis had already carried out.
  • The equation for incoming tourists has been tested by setting the value of the variables GDP and Population to zero in two different moments.
  • In both cases the value of the flow of tourists became zero as well.

4.2.4 Dimensional consistency test

  • Testing the dimensional consistency of a model simply means to compare the left and right sides of each of the equations formalized in the model to verify that the same units of measure are used for both sides.
  • This process is very long if the software used to build the model has not an automatic dimensional consistency verification functions.
  • Powersim, which is the software used for the present study, does not have an automatic function for consistency verification and, therefore, the consistency test has been carried out by checking the model variables manually one by one.

4.2.5 Structure oriented behavior tests

  • The structure of the proposed model has also been indirectly validated applying certain behavior tests on the model-generated behavior pattern.
  • The extreme-condition test has been carried out by setting the variables’ elasticity parameters inside each sector, separately considered, to the extreme values of zero.
  • In all the simulations, as expected, the sectors have shown a total rigidity towards the tourism demand for products and services.
  • In the same way, the attractiveness of the destination area has been evaluated in the case of a null exponent for 57 each of the variables representing a specific tourism aspect.
  • Instead, it has not been possible to do any modified-behavior predictions because data about the behavior of a modified version of the real system are not available.

4.3 Behavior validity

  • After having verified the level of confidence in the validity of the model structure, one can start measuring how accurately the model reproduces the major behavior patterns exhibited by the real system.
  • As this test assumes significance in terms of model validity only when it fails, the cases in which the model proved to be able to reproduce the desired behavior are not shown here.
  • Yet, since the emphasis of the behavior analysis is on the pattern prediction rather than on the point prediction, the model structure is considered able to reproduce the oscillations experienced in the real system, even if with a lower amplitude.

4.4 Sensitivity Analysis

  • Sensitivity basically consists of measuring the relative changes in the model behavior happening as one or more parameters’ values are modified.
  • This activity is crucial for the validity of the model.
  • Regarding the first objective of sensitivity analysis, the author has regularly performed sensitivity tests every time a new parameter was included in the model or some structures substantially changed.
  • Simulations are presented per sector of the model and the output of the simulation is compared with the real behavior.
  • The last set of simulations relates to the tourism flows from the specific international, national and local regions considered in the present study.

5.1 Simulation result in the hotel sector

  • Figure 5.1 compares the time series of the hotels in Sicily with the behavior simulated in the specific sector.
  • The red and the blue lines almost overlap, so the simulated trend in the number of hotels in Sicily reproduces the real values with very negligible deviations.
  • Figure 5.2 shows the behavior over time of the average number of bed-places per hotel in Sicily.
  • The possible consequences for the validity of the model of the difference between the real and the simulated values of bed-places have already been discussed in the validation chapter.

5.3 Simulation result in the culture sector

  • The culture sector allows the author some remarks.
  • Indeed, as shown in Figure 5.4, at the beginning and the end of the period considered, the simulated number of cultural resources (percentage of variation) and the real one show a different behavior.
  • Actually, the percentage of variation used as reference behavior, comes from data personally processed by the author.
  • In fact, the official site for cultural resources in Sicily presents a list of archaeological sites, museums, antiquaria, and so on, but does not indicate the year since when these resources are available to visitors.
  • Yet, since most of the behavior pattern is reproduced, the sector is of benefit for studying the determinants of tourism in Sicily.

5.4 Simulation result in the nature sector

  • As shown in Figure 5.5, the nature sector strictly reproduces the real behavior of natural resources over the simulation period.
  • In fact, the sector simulates very well the big increase in the number of natural resources recorded between 1999 and 2000 along with the general trend afterwards.

5.5 Simulation result in the urban environment sector

  • For the urban environment, the official statistics do not provide a time series summarizing its quality and behavior pattern over time.
  • Therefore, this paragraph is about the comparison between the real and the simulated behavior of the variables used to build the urban environment index in this study.
  • This time graph has already been discussed in the Chapter 4.
  • Figure 5.7, instead, represents the increasing trend in the number of vehicles registered in Sicily.
  • The time graph clearly shows the constant worsening of the general conditions of the urban environment.

5.6 Simulation result in the road sector

  • The simulation of the variable Roads over time reproduces quite well the observed values except for the year 2000.
  • A possible explanation of the higher real value in comparison with the simulated one has been given in Chapter 4.
  • Figure 5.9 shows the real and simulated behavior patterns.

5.7 Simulation result in the reputation sector

  • The reputation sector, as the urban environment sector, does not have a real behavior pattern to reproduce.
  • Indeed, there is not any official study concerning the reputation of Sicily.
  • The time graphs regarding the main variables used to build this sector, as described in Chapter 3, are presented to show, at least, how accurately these variables reproduce the behavior patterns exhibited by the real system.
  • Figure 5.10 shows the behavior over time of the real and simulated number of bag snatching and pick-pocketing in Sicily whereas Figure 5.11 presents the comparison between the real and simulated number of unemployed males in Sicily aged 15 and over.
  • The behavior of the variable Reputation over time is shown in figure 5.13.

5.8 French tourists

  • Figure 5.14 shows the real versus the simulated trend of French tourism in Sicily.
  • Except for the value in 2002 that looks one year deferred in the simulation, the sector reproduces quite well the general trend observed during the years.
  • In particular, the sector closely reproduces the growing number of French tourists in Sicily since 2005 resulting from the increased number of low cost flights from France to the international airports of Milan and Rome, and the increased number of low cost flights companies flying directly to the cities of Palermo and Catania.
  • Figure 5.14 shows the real and the simulated time series.
  • Real vs simulated French tourists in Sicily, also known as Figure 5.14.

5.9 German tourists

  • The simulation’s results for German tourists overlap the time series with great precision.
  • Specifically, the simulation accurately reproduces the decreasing trend in the number of German tourists recorded in Sicily in the period 2000-2003.
  • Figure 5.15 shows the real and the simulated results.

5.10 Norwegian tourists

  • Figure 5.16 reports the real and the simulated time series for Norwegian tourism.
  • The simulation is able to follow the general trend in the number of Norwegian tourists but not the observed values.
  • Actually, the case of Norwegian tourism in Sicily has to be treated differently from the others for being the Scandinavian tourism a new market for Sicily.
  • Generally speaking, indeed, it is normal for a new market to show some oscillations if compared to a consolidated market that, over time, generate a more stable and predictable demand.

5.13 Rest-of-Italy tourists

  • The behavior pattern showed by tourists travelling to Sicily from all the other Italian regions seems easily predictable with the specific sector introduced in the model.
  • In fact, the simulated behavior strictly follows the annual oscillations recorded in the first two year and, then, accurately reproduces the observed trend.

5.14 Local tourists

  • The simulated trend of local tourists, meaning Sicilian spending vacations in Sicily, shows a behavior able to reproduce the real one with great precision.
  • Actually, for several years there is not any relevant difference between the simulated values and the real ones.

5.15 Coefficients of the variables

  • The simulations’ result showed and discussed up to now are the result of the elasticity coefficient estimated for each variable in each demand function.
  • Looking at the results of the estimations in the direction of the rows, instead, four main considerations have to be done.
  • The increased number of hotels, restaurant and cultural resources did not produce any effect in terms of attractiveness of Sicily for the French market.
  • Norwegian tourism, as a new and growing market, presents a strong dependence on the arrivals of the past years (the variable related to the lagged arrivals).
  • The considerations regarding the characteristics of tourism in Sicily for the remaining studied countries are left to the reader.

Did you find this useful? Give us your feedback more

Content maybe subject to copyright    Report

A Dynamic Analysis of Tourism
Determinants in Sicily
Davide Provenzano
Master Programme in System Dynamics
Department of Geography
University of Bergen
Spring 2009

I am grateful to the Statistical Office of the European Communities (EUROSTAT); the Italian
National Institute of Statistics (ISTAT), the International Civil Aviation Organization (ICAO);
the European Climate Assessment & Dataset (ECA&D 2009), the Statistical Office of the
Chamber of Commerce, Industry, Craft Trade and Agriculture (CCIAA) of Palermo; the Italian
Automobile Club (A.C.I), the Italian Ministry of the Environment, Territory and Sea (Ministero
dell’Ambiente e della Tutela del Territorio e del Mare), the Institute for the Environmental
Research and Conservation (ISPRA), the Regional Agency for the Environment Conservation
(ARPA), the Region of Sicily and in particular to the Department of the Environment and
Territory (Assessorato Territorio ed Ambiente – Dipartimento Territorio ed Ambiente - servizio
6), the Department of Arts and Education (Assessorato Beni Culturali, Ambientali e P.I. –
Dipartimento Beni Culturali, Ambientali ed E.P.), the Department of Communication and
Transportation (Assessorato del Turismo, delle Comunicazioni e dei Trasporti – Dipartimento
dei Trasporti e delle Comunicazioni), the Department of Tourism, Sport and Culture
(Assessorato del Turismo, delle Comunicazioni e dei Trasporti – Dipartimento Turismo, Sport e
Spettacolo), for the high-quality statistical information service they provide through their web
pages or upon request.
I would like to thank my friends, Antonella (Nelly) Puglia in EUROSTAT and Antonino
Genovesi in Assessorato Turismo ed Ambiente – Dipartimento Territorio ed Ambiente – servizio
6, for their direct contribution in my activity of data collecting. Thanks to Dott. Antonino
Ballone in CCIAA for the same reason.
Many thanks to Prof. Carmine Bianchi for having introduced me to System Dynamics and to
Prof Francesco Andria for his support and the great opportunity he has given to me to continue
my research activity.
I am grateful to Prof. Pål Davidsen for having taught me System Dynamics and for his constant
support and encouragement.
I am grateful to my friend and colleague Valerio Lacagnina because most of the ideas and
insights implemented in this work are the result of the research activity we carry out together.

Many thanks to my friend Matteo Pedercini for his support and help even from abroad.
Finally, there are four women I owe very deep thanks to.
My true mentor, Prof. Maria Caliri, who gave me the opportunity to start my academic career
and allowed me to spend one year in Bergen where I studied System Dynamics.
My extraordinary grandmother, Francesca, who has always supported me with her love and
My great mother, Caterina, for her strength, her courage, her advice and unique and personal
way to be a mother.
My sweet girlfriend Giulia for the special meaning she has given to my life.
Without this unique poker of Queens this thesis would not exist. To them this thesis is dedicated.

and Giulia.

More filters

Journal ArticleDOI
Abstract: This research investigates the determinants of the attractiveness of urban tourism in two recognized tourist and heritage cities, Quebec City and Bordeaux. In an analysis based on a tripartite theoretical model of attractiveness, a statistical comparative analysis was done in both cities to examine visitors’ perceptions using a questionnaire survey completed by about 500 visitors. This analysis produced three main findings. First, a factor analysis demonstrated that tourists recognize attractiveness on four levels (context, tourist belt, complementary attractions and nucleus). Second, elements linked to the tourist belt, such as public spaces and the urban environment, are viewed as the most important. Third, mean value comparisons revealed that tourists who consult the Internet and social media have heightened sensitivity to nucleus, complementary attraction and context levels. To stimulate tourism attractiveness, cities’ communications should thus emphasize the elements associated with the tourist belt, and effectively utilize Internet and social media to convey its caracteristics. Because urban public areas play an essential role in tourism attractiveness, it is also recommended that cities incorporate tourism into their planning strategy.

24 citations

Journal ArticleDOI
Abstract: Tourism is one of the prime manifestations of the ‘great acceleration of humankind’ since the Anthropocene started around 1950. The almost 50-fold increase in international tourism arrivals has substantial implications for environmental sustainability, but these have not yet been fully explored. This paper argues that a full exploration requires the study of tourism as a complex socio-ecological system. Such approach integrates environmental processes and stakeholder behaviour and puts feedbacks in the spotlight. Systemic insights can inform strategies to address tourism's problematic environmental performance. The paper finds that systems approaches in tourism research are rare and identifies a number of challenges: the large number of stakeholders involved; the heterogeneity of stakeholders; and the lack of transdisciplinarity in tourism research. The paper then argues that agent-based modelling can help address some of these challenges. Agent-based modelling allows to run simplified tourism systems with heterogeneous stakeholders and explore their behaviour, thus acting as living hypotheses. They do this by: (1) representing tourism's dynamics in a systemic, intuitive and individual-based way; (2) combining theories from different domains; (3) unpacking the link between stakeholder behaviours and emergent tourism system patterns; and (4) connecting researchers and stakeholders. Agent-based models allow representation of heterogeneous agents driven by plausible needs, who perceive local context and interact socially. Companion modelling is identified as a promising tool for more effective stakeholder inclusion.

21 citations

Journal ArticleDOI
17 Apr 2018
Abstract: Purpose This paper aims to investigate the determinants of international bilateral tourism demand in countries of Southern Common Market (specifically, Argentina, Brazil and Uruguay) and Chile. Design/methodology/approach In this study, an augmented gravity model is used to investigate the determinants of international bilateral tourism demand in countries of Southern Common Market. The novel aspect of the analysis is that three models of tourism are defined, depending on the spatial distribution of tourist arrivals and departures. An intra-regional model, an extra-regional model and a general model are estimated using a dynamic panel data model. Findings The results indicate that traditional gravity variables are significant in explaining bilateral inbound arrivals, but the characteristics and the behavior of the demand of tourism vary on whether the country belongs to the sub-regional bloc. Research limitations/implications The differences found in this paper might have some impacts on the desired design and direction of the touristic policies of each country. Originality/value This study analyzes the determinants of international tourism demand through different bilateral relationships, differentiating between intra- and extra-block tourisms.

15 citations

Journal ArticleDOI
Abstract: Climate variables such as temperature and precipitation play a crucial role on tourism flows worldwide. This places tourism at the forefront of the economic sectors to be affected by climate change. In this article, we address the impacts of climate change on the arrivals of inbound tourists to Portugal, a south European country where tourism is a core economic sector. The economic dimension of the impacts, in terms of gross domestic product (GDP) and employment, is then assessed. This is achieved by combining a world gravity model of tourism flows with an input–output model. The results show that under standard climate change scenarios from the Intergovernmental Panel on Climate Change, Portugal will experience a significant increase in temperature leading to a decrease of inbound tourism arrivals between 2.5% and 5.2%. This decrease in tourist arrivals is expected to reduce Portuguese GDP between 0.19% and 0.40%.

14 citations

Cites background from "A Dynamic Analysis of Tourism Deter..."

  • ...…have also been used to estimate the magnitude of tourism flows in different contexts (Eilat and Einav, 2004; Fourie and Santana-Gallego, 2011; Fourie et al., 2015; Gálvez et al., 2014; Khadaroo and Seetanah, 2008; Kimura and Lee, 2006; Provenzano, 2015; Santana et al., 2010; Vietze, 2012)....


Journal ArticleDOI
Abstract: This paper proposes a two-step approach for analysing the main determinants of multi-destination trip behaviour. It is based on a combination of graphical models and of a multinomial logistic regression model; the aim is to analyse direct and indirect effects of a wide set of tourist- and trip-related characteristics on multi-destination trip behaviour. Empirical data have been derived from a frontier survey of approximately 4000 incoming tourists in Sicily (Italy) at the end of their trip. Results suggest that multi-destination trips depend directly on the length of stay, the number of previous visits and motivation for the trip, and only indirectly on the interview month, travel party, organization of the trip, and country of origin. The role of other socio-demographic characteristics, such as age or gender is confirmed to be marginal or even irrelevant.

8 citations

More filters

Journal ArticleDOI
Abstract: Despite the gravity equation's empirical success in "explaining" trade flows, the model's predictive potential has been inhibited by an absence of strong theoretical foundations. A general equilibrium world trade model is presented from which a gravity equation is derived by making certain assumptions, including perfect international product substitutability. If, however, trade flows are differentiated by origin as evidence suggests, the typical gravity equation is misspecified, omitting certain price variables. The last section presents empirical evidence supporting the notion that the gravity equation is a reduced form from a partial equilibrium subsystem of a general equilibrium model with nationally differentiated products. THE "gravity equation" has been long recognized for its consistent empirical success in explaining many different types of flows, such as migration, commuting, tourism, and commodity shipping. Typically, the log-linear equation specifies that a flow from origin i to destination j can be explained by economic forces at the flow's origin, economic forces at the flow's destination, and economic forces either aiding or resisting the flow's movement from origin to destination. In international trade, bilateral gross aggregate trade flows are explained commonly using the following specification: PXi, = fo(yi) (I?) 2(Dij )3(A 1)4u (1) where PXij is the U.S. dollar value of the flow from country i to country j, Yi (Y1) is the U.S. dollar value of nominal GDP in i (j), Dij is the distance from the economic center of i to that of j, Aij is any other factor(s) either aiding or resisting trade between i and j, and u is a log-normally distributed error term with E(ln uij) = 0. This specification was used in Tinbergen (1962), Poyhonen (1963a, 1963b), Pulliainen (1963), Geraci and Prewo (1977), Prewo (1978), and Abrams (1980).1 Table I presents results from estimating a gravity equation similar to (1) for 15 OECD countries' trade flows.2 Coefficient estimates are stable across years and are representative of trade gravity equations. Despite the model's consistently high statistical explanatory power, its use for predictive purposes has been inhibited owing to an absence of strong theoretical foundations. The most common justification-used in Linnemann (1966), Aitken (1973), Geraci and Prewo (1977), Prewo (1978), Abrams (1980), and Sapir (1981)-was developed by Linnemann and asserts that the gravity model is a reduced form from a four-equation partial equilibrium model of export supply and import demand. Prices are always excluded since "they merely adjust to equate supply and demand."3 However, critics have argued that this approach is "loose" and does not explain the multiplicative functional form.4 This study addresses these and other issues in developing further the microeconomic foundations of the gravity equation. The "looseness" critique is addressed by systematically describing assumptions necessary to generate a gravity equation similar to (1) from a general equilibrium framework. Specific, yet intuitively plausible, functions for utility and production generate the equation's multiplicative form. Section I presents a general equilibrium model of world trade derived from utilityand profit-maximizing agent behavior in N countries assuming a single factor of production in Received for publication June 16, 1983. Revision accepted for publication December 12, 1984. *Federal Reserve Bank of Boston. The author is very grateful to J. David Richardson, Robert Baldwin, Rachel McCulloch, James Alm, Saul Schwartz and two anonymous referees for helpful comments on earlier drafts, and Doug Cleveland for research assistance. All errors remain the author's responsibility. The views expressed do not necessarily reflect the views of the Federal Reserve Bank of Boston or the Federal Reserve System. 'Linnemann (1966), Aitken (1973), Sattinger (1978) and Sapir (1981) used the same general specification, but also included exporter and importer populations. Microeconomic foundations of this alternative specification are discussed in Bergstrand (1984). 2The countries are Canada, United States, Japan, BelgiumLuxembourg, Denmark, France, West Germany, Italy, Netherlands, United Kingdom, Austria, Norway, Spain, Sweden, and Switzerland. The adjacency, EEC, and EFTA dummies are explained in the appendix. 3Linnemann (1966), p. 41; Leamer and Stern (1970), p. 146; (Geraci and Prewo (1977), p. 68; Prewo (1978), p. 344; and Sapir (1981), p. 341. 4See, for example, Anderson (1979), p. 106 and Leamer and Stern (1970), p. 158.

2,873 citations

Journal ArticleDOI
J Swanson1
TL;DR: This book is most obviously relevant to practitioners who already have some experience of multiagency facilitation, but might also serve as an introduction to working in this arena, if carefully supplemented with further reading and exploration of the topics it covers.
Abstract: (2002). Business Dynamics—Systems Thinking and Modeling for a Complex World. Journal of the Operational Research Society: Vol. 53, No. 4, pp. 472-473.

2,697 citations

"A Dynamic Analysis of Tourism Deter..." refers background in this paper

  • ...For the definition of CLD, reinforcing loop and balancing loop see Sterman (2000)....


Journal ArticleDOI
Abstract: Contemporary studies of tourism see the tourist experience as either something essentially spurious and superficial, an extension of an alienated world, or as a serious search for authenticity, an effort to escape from an alienated world. It is argued that neither of these views is universally valid. A more discriminating distinction between five types of tourist experiences is proposed, based on the place and significance of tourist experience in the total world-view of tourists, their relationship to a perceived `centre' and the location of that centre in relation to the society in which the tourist lives. It is proposed that the resulting continuum of types of tourist experience is both more comprehensive than alternative conceptual frameworks and capable of reconciling and integrating the conflicting interpretations arising from earlier studies.

1,732 citations

Journal ArticleDOI
TL;DR: This paper focuses on the formal aspects of validation and presents a taxonomy of various aspects and steps of formal model validation, including structure-oriented behavior tests, which seem to be the most promising direction for research on model validation.
Abstract: Model validation constitutes a very important step in system dynamics methodology. Yet, both published and informal evidence indicates that there has been little effort in system dynamics community explicitly devoted to model validity and validation. Validation is a prolonged and complicated process, involving both formal/quantitative tools and informal/qualitative ones. This paper focuses on the formal aspects of validation and presents a taxonomy of various aspects and steps of formal model validation. First, there is a very brief discussion of the philosophical issues involved in model validation, followed by a flowchart that describes the logical sequence in which various validation activities must be carried out. The crucial nature of structure validity in system dynamics (causal-descriptive) models is emphasized. Then examples are given of specific validity tests used in each of the three major stages of model validation: Structural tests, structure-oriented behavior tests and behavior pattern tests. Also discussed is if and to what extent statistical significance tests can be used in model validation. Among the three validation stages, the special importance of structure-oriented behavior tests is emphasized. These are strong behavior tests that can provide information on potential structure flaws. Since structure-oriented behavior tests combine the strength of structural orientation with the advantage of being quantifiable, they seem to be the most promising direction for research on model validation.

1,235 citations

"A Dynamic Analysis of Tourism Deter..." refers background or methods in this paper

  • ...…process of the formal structure of the model (that is, the form of the equations, their validity under extreme conditions, the dimensional consistency and the sensitivity of the model behaviour to parameter values) was carried out as proposed by Barlas (1996)6 during the parameter estimation phase....


  • ...Apart from the parameter confirmation test, most of the validation process of the formal structure of the model (that is, the form of the equations, their validity under extreme conditions, the dimensional consistency and the sensitivity of the model behaviour to parameter values) was carried out as proposed by Barlas (1996)(6) during the parameter estimation phase....


  • ...Data generated by system dynamics models are instead autocorrelated and cross-correlated by their very nature (Barlas, 1996)....


  • ...Most of the validation tests described in Barlas (1996) were carried out at this stage of the model development....


Journal ArticleDOI
Abstract: This paper reviews the published studies on tourism demand modelling and forecasting since 2000. One of the key findings of this review is that the methods used in analysing and forecasting the demand for tourism have been more diverse than those identified by other review articles. In addition to the most popular time-series and econometric models, a number of new techniques have emerged in the literature. However, as far as the forecasting accuracy is concerned, the study shows that there is no single model that consistently outperforms other models in all situations. Furthermore, this study identifies some new research directions, which include improving the forecasting accuracy through forecast combination; integrating both qualitative and quantitative forecasting approaches, tourism cycles and seasonality analysis, events' impact assessment and risk forecasting. (C) 2007 Elsevier Ltd. All rights reserved.

891 citations