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Scandinavian Journal of Hospitality and Tourism

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Can the Butler's Tourist Area Cycle of EvolutionBe Applied to Find the Maximum Tourism Level? AComparison of Norway and Iceland to Other OECDCountries

Helga Kristjánsdóttir

To cite this article: Helga Kristjánsdóttir (2016) Can the Butler's Tourist Area Cycle of EvolutionBe Applied to Find the Maximum Tourism Level? A Comparison of Norway and Iceland toOther OECD Countries, Scandinavian Journal of Hospitality and Tourism, 16:1, 61-75, DOI:10.1080/15022250.2015.1064325

To link to this article: http://dx.doi.org/10.1080/15022250.2015.1064325

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Can the Butler’s Tourist Area Cycle ofEvolution Be Applied to Find theMaximum Tourism Level? A Comparisonof Norway and Iceland to Other OECDCountries

HELGA KRISTJANSDOTTIR

University of Iceland, Iceland

ABSTRACT This research seeks to analyze the S-shape of the Butler’s tourist area cycle ofevolution in order to capture the maximum tourist level. It is the first time this type ofeconomic regression modeling is performed for the Butler’s tourist area cycle of evolution,referred to as the tourism area life cycle (TALC) model. Also, this is the very first time thecycle is applied to forecast a potential peak in inbound tourists in a particular country andsample of countries. To capture the non-monotonic relationship of the cycle, a fifth-degreepolynomial is put forward, accounting for government, banks, roads, skilled labor, andInternet application. Results indicate that the S-shape of the Butler’s tourist area cycle ofevolution can be captured with a polynomial function for a range of OECD countries, aswell as for Norway and Iceland combined and for Iceland solely. This can be interesting aswell as useful for tourism researchers seeking to explain the flow of tourists. The mainimplication of this study to managers and tourism policy planners is the potential to applythe TALC model to estimate development and potential peaks in the tourism industry inadvance, years before the tourist level reaches maturity at the top.

KEY WORDS: Butler’s tourist area cycle of evolution, trade, OECD, time-series analysis,fifth-degree polynomial

Introduction

In recent years, tourism has been regarded among the biggest and most expandingindustries in the world (OECD, 2008). The evolution of the tourism industry isdescribed in Butler’s (1980) tourist area cycle of evolution. In his research, Butler(1980) seeks to explain the implications for management of resources, introducingthe concept of a tourist area cycle of evolution, also referred to as the tourism arealife cycle (TALC) model. Furthermore, in his discussion of tourism in the future,

Correspondence Address: Helga Kristjansdottir, University of Iceland, Gimli, Post box 32, Sæmundargotu2, 101 Reykjavık, Iceland. E-mail: helgakr@hi.is

Scandinavian Journal of Hospitality and Tourism, 2016Vol. 16, No. 1, 61–75, http://dx.doi.org/10.1080/15022250.2015.1064325

# 2015 Taylor & Francis

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Butler (2009) poses the following question: cycles, waves, or wheels? When referringto the shape of the curve for tourist resort evolution. Tourism is viewed as economicactivity by Butler (2009) in an attempt to derive the evolvement of tourist resorts.Gilbert (1939) and Christaller (1963) present three phases in the development oftourist destinations: (1) discovery, (2) growth with rising demand, and (3) declinewith decreasing demand. Manente and Pechlaner (2006) discuss the potential use ofthe TALC model for the predictability of the demand for tourist resorts, taking intoaccount the economic role of tourism, among other things, in the TALC model.Also, Berry (2006) discusses whether it is possible to test the different evolvementstages of tourist resorts using the TALC model when testing the different stages ofthe TALC model. Lozano, Gomez, and Rey-Maquieira (2008) explain how theButler TALC model relates to the growth theory in economics, using numerical calcu-lations to explain the tourism pattern; they find the evolution of tourism destinations tobe limited by environment decline and public good availability, eventually leading tostagnation.

Also, Almeida and Correia (2010) seek to determine the connection between theButler TALC model and economic forecasting and analyze tourism in the Spanishisland Madeira using the TALC model. Their findings indicate that Madeira tourismgrowth, in accordance with the TALC model, cannot continue further and that thelocal tourism is reaching its phase of maturity. Their analysis covers the time periodof 1976–2006.

This current study includes a North Atlantic perspective, with a specific focus onNorway and Iceland, countries receiving attention in recent analysis by Baldacchino,Helgadottir, and Mykletun (2015), Sigurðardottir and Helgadottir (2015), andEngeset and Heggem (2015). The main research question of interest here is thus:Can Butler’s (1980) tourist area cycle of evolution be applied to predict the peak ofthe tourism industry? This research seeks to answer the question by presenting theS-shape of the cycle. The S-shape presentation is in line with Butler’s presentationof the cycle; it allows for determination of growth, first increasing slowly and thenincreasing more steeply until it reaches maturity at the top. The approach involvesthe use of a polynomial, assuming an end of the growth phase at the time of maturity,thus leading to the maximum point. An attempt is made here to predict the time of themaximum point, with an application of a fifth-degree polynomial, which accounts forthe S-shape of the curve.

Butler (1980) provides an extension over six phases using the tourist area cycle ofevolution to account for similarities in the product life cycle (PLC) (Getz, 1992).The model views tourism as a means of resource exploitation. The six stages in theButler’s (1980) tourist area cycle of evolution are exhibited in Figure 1. These stagesare: (1) exploration, (2) involvement, (3) development, and (4) consolidation. Theseare followed by (5) stagnation and finally (6) decline or rejuvenation.

In his article, Butler suggests that the tourist area cycle of evolution is based on thePLC.

The S-shape curve is also visible in Figure 2(a) and 2(b). The figures exhibit thebusiness life cycle, with Figure 2(b) accounting for revenues and profits. The shapeof the cycle corresponds with the shape of the Butler’s (1980) tourist area cycle ofevolution.

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In the current research, economic regression estimates are obtained for a range ofOECD countries, the Nordic countries of Norway and Iceland combined, and the par-ticular case of the country of Iceland.

Some researchers have sought to use Zipf’s law of tourism to predict tourist arrivals(Ulubasoglu & Hazari, 2004). The Zipf’s law is used when determining the event fre-quency, indicating how common a particular event is. Another common distribution,the Pareto distribution, is well known when determining the distribution of income

Figure 1. Butler’s (1980) tourist area cycle of evolution (sources: Butler, 1980; LifeUncharted Travel, 2012).

Figure 2. (a) Business life cycle (source: Advantage Woman, 2013). (b) Product life cycle(source: MR Dashboard, 2013).

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(Jagielski, Duczmal, & Kutner, 2015; Yang, Zhang, Liu, Li, & Wan, 2007). Also, theturning points, indicating the change from a decrease to an increase and vice versa, ofthe product lifecycle model have been studied (Huang & Tzeng, 2008), and regressionmodels for the product lifecycle model are presented (Liu, Gopalkrishnan, Quynh, &Ng, 2009).

Aguilo, Alegre, and Sard (2005) seek to estimate Butler’s tourist area cycle of evol-ution (1980) for the Balearic Islands. Also, Lundtorp and Wanhill (2001) make anattempt to formulate an explanation of the lifecycle model. Their approach to the expla-nation is that it is demand driven, indicating that the cycle evolvement is primarilyexplained by the demand of tourists, rather than the supply associated with resorts.

Furthermore, the Butler’s tourist area cycle of evolution is applied to Catalonia inSpain (Garay & Canoves, 2011). When explaining the evolvement stages, Butler(1980) mentions carrying capacity, land scarcity, water quality, air quality, transpor-tation, and accommodation. Butler (1980) also refers to social factors such as crowdingdislike and local knowledge. Garay and Canoves (2011) find the Butler cycle to accountfor economic as well as the territorial explanation of tourism. However, these previousstudies generally do not associate the Butler’s (1980) tourist area cycle of evolutionwith economics, like this current research does.

First, the analysis for the OECD countries is provided, and the research is extended toprovide a comparison for Norway and Iceland to other OECD countries. Finally, thefact that Butler (2009) concludes that the tourist area cycle of evolution “works wellwith destinations established in earlier days” may suggest that the model couldpredict well for a country with a recently developed tourist market, like Iceland.

Methodology

Iceland has been classified at the top of emerging destinations in Europe, due to the factthat it experienced a 20% growth in foreign visits in the year 2012 (European TravelCommission, 2013).

This is the very first time Butler’s (1980) tourist area cycle of evolution is beingmodeled economically to test the S-shape of the cycle and is applied to forecastwhen it is likely to reach a certain maximum level in the number of tourists arrivingin a particular country. Also, the OECD country comparison is unique in this currentresearch.

To capture the S-shape of the Butler’s tourist area cycle of evolution, it is particularlyinteresting to consider how the change between phases is marked by the turning points.The turning points are points of inflection. They determine when the functionalform changes from concave to convex. More specifically, the convex part presentsthe U-shaped relationship; however, the concave part the >-shaped relationship.

Figure 3 shows the interpretation of the tourist development stages proposed byLundtorpa and Wanhill (2001). Figure 3 shows the relative number of tourists, withthe relative number 0.09 presenting 9% of the tourist maximum of 100%, proceedingto 0.21 (21%), 0.79 (79%), and 0.91 (91%). Finally, the top (upper-right) of the curvecorresponds to 100%, and likewise the bottom (lower-left) to 0%. Because of thebell-shape or S-shape, the turning point is in t0, where the slope goes from beingpositive and increasing to being decreasing. Because of the S-shape of the curve, the

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relative numbers on each side of t0 correspond to each other, with t1 and t2 being equallydistant from t0 (with 0.21 + 0.79 ¼ 1 and 0.09 + 0.79 ¼ 1). The curve is (symmetric)around the center t0, and numbers equally distant from t0 on either side have a sum of 1,with the sum of t1 and t2 being 1, for example.

This current research interprets stage (1) exploration and the involvement stage (2)and stage (3). Development in the following way; the first derivative is positive andthe second derivative is also positive, indicating that the slope of the function is positiveand increasing:

f ′(x) . 0 f ′′(x) . 0. (1)

However, in stage (4) consolidation, and stage (5) stagnation, the slope is positive butdecreasing, and it holds that

f ′(x) . 0 f ′′(x) , 0. (2)

These are followed by stage (6) decline, with a negative and decreasing slope,presented as

f ′(x) , 0 f ′′(x) , 0. (3)

Or stage (6) rejuvenation, with a positive and increasing slope, where it holds that

f ′(x) . 0 f ′′(x) . 0. (4)

The turning points, or points of inflection, can potentially be used to determine whencountries are likely to reach the potential maximum point of tourists when entering into

Figure 3. The relative number of tourists (source: Lundtorpa & Wanhill, 2001).

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the stagnation or maturity stages. This current research assumes that, by the end of theperiod, there is a decline rather than rejuvenation (Butler, 1980). This is presumed to bepessimistic rather than optimistic, since it is not possible to assume both a decline andrejuvenation.

The research then continues by presenting an equation to capture the shape of thePLC. The polynomial equation is chosen for estimation, since the polynomial shapeaccounts for the S-shape of the Butler’s tourist area cycle of evolution.

The PLC theory can be regarded as a dynamic theory of international trade(Carbaugh, 2011). This connection is provided here by linking the modeling setupwith international trade modeling. Based on Bergstrand (1985), modeling exports isthe trade-dependent variable here, since tourism is one form of export, and thereforeone form of international trade.

Within the trade literature, it is common to analyze trade flow to one particularcountry, like in the case of Tekin-Koru and Waldkirch (2010), who seek to explainheavy reliance on trade by country. In the same setting, this current research focuseson tourist flow as a form of trade flow in one particular country.

The polynomial equation for estimation is presented in Equation (5):

exports ij,s,t = b0 + b1x1ij,s,t + b2x2

ij,s,t + b3x3ij,s,t + b4x4

ij,s,t + b5x5ij,s,t + 1 ij,s,t, (5)

where the sector notation, denoted with s, is set to account for the tourist industryspecifically. Since the sector is fixed to solely account for the tourist sector, the S deno-tation in the dependent variable and error term is not necessary. The equation thereforebecomes as found in Equation (6):

touristsij,t = t0 + t1x1ij,t + t2x2

ij,t + t3x3ij,t + t4x4

ij,t + t5x5ij,t + zij,t. (6)

The export equation has therefore been narrowed down as to solely account forexport in the tourist sector from country j to i, in a particular sector denoted with s.By solely focusing on the tourist sector, the s notation is not necessary, since it nowsolely accounts for the tourist sector. Therefore, the inflow of tourists from country ito country j is denoted with ij over time t.

The World Economic Forum report on Travel and Tourism Competitiveness (WEF,2013) lists a range of factors effecting competitiveness. One of these factors is trans-parency of government policymaking. Therefore, the government variable is chosenfor application in this current research. Furthermore, WEF accounts for the factors ofprevalence of foreign ownership, property rights, and business impact of rules onFDI, and to account for these factors, the variable bank is included.

Moreover, WEF incorporates factors accounting for quality of roads and roaddensity, and therefore the paved roads variable is used here. Also, factors such asprimary education enrollment, secondary education enrollment, quality of the edu-cational system, local availability of specialized research and training services, andextent of staff training are included by WEF; therefore the variable skilled labor isapplied in this research.

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Furthermore, the WEF incorporates factors accounting for Information and Com-munications Technology, or ICT use for business-to-business transactions, Internetuse for business-to-consumer transactions, individuals using Internet and broadbandInternet subscribers. Therefore the variable internet is incorporated in this currentresearch.

The factors accounted for in this current research are infrastructure-related factorssuch as paved roads and Internet access (World Bank, 2013) and macro-economicfactors such as government efficiency. Also, the central bank policy (IMD, 2012) isincluded as well as the quality of labor measured with skilled labor (IMD, 2012).

The tourist equation is then re-written; so it becomes the following specification:

touristsij,t = g0 + g1governmenti,t + g2bank2i,t + g3paved roads3

i,t

+ g4skilled labor4i,t + g5internet5i,t + hij,t. (7)

In the setting of the PLC, skilled labor is presented in the following equation:

nightsij,t = k0 + k1governmenti,t + k2bank2i,t + k3paved roads3

i,t

+ k4skilled labour4i,t + k5internet5i,t + jij,t, (8)

where it is presumed that the error term 1ij is log-normally distributedwith E(ln 1ij) = 0.

occupancyij,t = u0 + u1governmenti,t + u2bank2i,t + u3paved roads3

i,t

+ u4skilled labour4i,t + u5internet5i,t + vij,t. (9)

Evolvement of resort areas through time has gained the attention of researchers suchas Kostiainen (2007), focusing on the Northern Riviera life cycle for the Terijoki resortarea close to St. Petersburg. The Terijoki area development is studied over three periodsof time: first, when governed by the Russian Czar, second, then, governed by Finlandauthorities, and third when under Soviet, or Russian, governance. These three phasesare viewed as cycles, separated by political changes or wars. Although locateddistant from other places, Terijoki is referred to as the Northern Rivera to underlineits “dignified and attractive location.”

Here the focus is on the Northern countries, Norway and Iceland, in comparison to arange of OECD countries. The sample therefore covers data on these countries. Thesecountries are selected since they are believed to provide sufficient variation; this isreflected in the country sample selection. Variation is based on different country andpopulation sizes.

Data on the variable nights are obtained from Eurostat (2013) for the followingcountries: Austria, Belgium, Czech Republic, Denmark, Estonia, Finland, France,Germany, Greece, Hungary, Iceland, Ireland, Italy, Luxembourg, Netherlands,Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, and the UK.Guest-nights are presented as nights in the regression equation and account for the

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total nights spent in each country by non-residents in collective tourist accommodationestablishments.

Table 1 explains all the variables used. The variable occupancy accounts for the netoccupancy rate in each country and of bed places in hotels and similar establishments,and the data on occupancy are obtained from the Eurostat (2013) for all countriesexcept Iceland, the data for which are obtained from Statistics Iceland (2013). Datafrom Eurostat (2013) cover hotels and similar establishments. However, data fromStatistics Iceland (2013) only covers hotels, with hotels accounting for the vast majorityof accommodation in Iceland. Data on occupancy are obtained for the followingcountries: Austria, Belgium, Denmark, Finland, France, Germany, Greece, Iceland,Ireland, Italy, Luxembourg, Netherlands, Norway, Portugal, Spain, Sweden, Switzer-land, and the UK.

Data on the dependent variable tourists span the number of tourists arriving inIceland. Data on tourists are from the Icelandic Tourist Board (2012), runningthrough the period of 2003–2011 and spanning over 93% or more of foreign visitorspassing through Keflavik International airport every year, registering their nationalities.The sample of countries is a result of the nationalities; the countries included are the

Table 1. Variable definition.

Variable Description DefinitionSource of data incurrent research

Nights Guest-nights Total nights spent by non-residents Eurostat (2013)

Occupancy Occupancy rate Net occupancy rate of bed places inhotels and similar establishments

Eurostat (2013)and StatisticsIceland (2013)

Tourists Internationaltourists

Number of international tourists.Inbound tourists, from origincountries (i) to host country ( j),over time (t)

Icelandic TouristBoard (2012)

Government Governmentefficiency

Government decisions are effectivelyimplemented. Index from 0 to 10

IMD (2012)

Bank Central bankpolicy

Central bank policy has a positiveimpact on economic development.Index from 0 to 10

IMD (2012)

Paved roads Paved roads % oftotal roads

Paved roads are those surfaced withcrushed stone (macadam) andhydrocarbon binder or bituminizedagents, with concrete, or withcobblestones, as a percentage of allthe country’s roads, measured inlength

World Bank (2013)

Skilledlabor

Skilled labor Skilled labor is readily available.Index from 0 to 10

IMD (2012)

Internet Internet users, per100 people

Internet users are people with accessto the worldwide network

World Bank (2013)

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following: Canada, Denmark, Finland, France, Germany, Italy, Japan, the Netherlands,Norway, Spain, Sweden, Switzerland, the UK, and the USA.

The variable government accounts for government efficiency. More specifically, itaccounts for the phrase “Government decisions are effectively implemented.” Theindex runs from 0 to 10 and is obtained from the IMD (2012). The bank variableaccounts for central bank policy, or more specifically, “Central bank policy has a posi-tive impact on economic development.” The index runs from 0 to 10 and is obtainedfrom the IMD (2012).

One of the things Butler (1980) addresses when discussing the TALC model is theimportance of transportation; therefore, one of the variables here accounts for pavedroads. The variable paved roads represents paved roads as a percentage of totalroads. Paved roads are those surfaced with crushed stone (macadam) and hydrocarbonbinder or bituminized agents, with concrete or with cobblestones, as a percentage of allof the country’s roads, measured in length. The variable source is received from theWorld Bank (2012). The World Economic Forum (2013) Travel & Tourism Competi-tiveness Report stresses the importance of including quality of roads, when consideringground transport infrastructure and when reporting travel and tourism competitivenessof nations. The World Bank infrastructure definition applied in this current researchincludes paved roads, among other infrastructure factors. Paved roads are an importantpart of infrastructure in countries as an indicator of the accessibility of a country and areflection of the level of the available infrastructure. More specifically, the WorldBank’s (2012) definition is “Roads, paved (% of total roads). Paved roads are those sur-faced with crushed stone (macadam) and hydrocarbon binder or bituminized agents,with concrete, or with cobblestones, as a percentage of all the country’s roads,measured in length.”

The skilled labor variable represents skilled labor. Skilled labor is a readily availableindex from 0 to 10. The variable is obtained from the IMD (2012).

The internet variable accounts for Internet users per 100 people. Internet users arepeople with access to the worldwide network. The source for these variables is theWorld Bank (2013).

Results

Figure 4 exhibits the number of inbound tourists in Iceland during the time period1949–2011, with a rapid increase in recent years. The figure shows that the numberof tourists has been growing rapidly in recent years.

Figure 5 illustrates how the growth in the number of tourists is impacted by the inno-vation S-curve. New product varieties result in small-S-shapes in the overall develop-ment phases.

The regression results obtained from running the statistical software STATA 10 arepresented in Table 2 through Table 4.

The estimates obtained for the large OECD sample in Table 2 indicate that pavedroads have significant positive effects on both nights and occupancy, and the Internetdoes not have significant effect. However, estimates for other variables are mixed, sincegovernment is estimated to have positive effects on nights, but negative effects on theoccupancy rate. The bank variable is estimated to have significant negative effects on

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nights but positive effects on occupancy rate. Finally, skilled labor in the sourcecountry is estimated to have significant negative effects on occupancy rate but insignif-icant effects on nights.

The research continues by analyzing data for Iceland and Norway. Estimation resultsin Table 3 are obtained by running regressions for guest night stays by foreigners in thetwo countries as well as the occupancy rate in the two countries. Results indicate thatthe condition in terms of government and banks does not have significant effects onnight stays or occupancy in the two countries. The variable presenting condition ofroads has positive significant effects in both countries, and skills and the Internethave negative effects, although only significantly so on guest nights.

Regression results for Table 4 indicate that the government in the source country oftourists has significant negative effects on tourist inflow to Iceland; however, the bankstatus has significantly positive effects, and so does the paved road infrastructure and

Figure 4. Number of inbound tourists in Iceland in the time period 1949–2011 (source: Stat-istics Iceland, 2012).

Figure 5. The innovation S-curve (source: Dean McMann, 2013).

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Table 2. OECD sample estimation results.

Regressors (i) Nights (ii) Occupancy

Government 115270.6(0.04)

228.856∗∗

(22.12)

Bank 2896667.2∗∗∗

(22.85)5.274∗∗∗

(4.43)

Paved roads 53.177∗∗∗

(6.87).0001675∗∗∗

(5.06)

Skills 2494.066(0.91)

2.025∗∗

(22.06)

Internet 2.001(20.49)

23.75e-09(20.51)

Constant 2.87e+07∗∗∗

(3.23)309.568∗∗∗

(5.80)

R2 0.3371 0.5459

Obs 108 101

Note: Robust t-statistics reported in parentheses.∗∗∗Significant at the 1% level.∗∗Significant at the 5% level.∗Significant at the 10% level.

Table 3. Guest-nights and occupancy rate in Iceland and Norway.

Regressors (i) Nights (ii) Occupancy

Government 64988.17(1.47)

2.291(0.41)

Bank 1694.077(0.26)

21.088(20.71)

Paved roads 12.068∗∗∗

(22.33).001∗∗∗

(6.80)

Skills 2266.876∗∗∗

(23.44)2.002(20.19)

Internet 2.0001∗∗

(21.97)21.50e-08

(21.53)

Constant 2035569∗∗∗

(5.40)80.146(1.35)

R2 0.9985 0.9619

Obs 11 15

Note: Robust t-statistics reported in parentheses.∗∗∗Significant at the 1% level.∗∗Significant at the 5% level.∗Significant at the 10% level.

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the Internet. Moreover, the level of skilled labor is not estimated to have significanteffects on tourist inflow to the country.

The red line in Figure 6 shows the increase in the tourists in Iceland, in real numbers,and the black line shows the predicted forecast calculated by the author.

Table 4. Flow of tourists to Iceland.

Regressors Tourists

Government 29467.783∗∗∗

(22.88)

Bank 980.671∗∗∗

(3.85)

Paved roads .012∗∗

(2.00)

Skills 25.058(21.60)

Internet 3.55e206∗∗

(2.26)

Constant 27908.52∗∗∗

(2.73)

R2 0.3870

Obs 59

Note: Robust t-statistics reported in parentheses.∗∗∗Significant at the 1% level.∗∗Significant at the 5% level.∗Significant at the 10% level.

Figure 6. Forecasted number of inbound tourists in Iceland (source: Statistics Iceland, 2012and author’s calculations).

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Discussion and Conclusion

This research on tourist resorts is based on the previous contributions: first, Butler pre-sented a study on tourist resort evolvement in 1980. Second, Manente and Pechlaner, in2006, take into account the economic role of tourism in the TALC model. Third, Berry,in 2006, discusses the evolvement stages of tourist resorts using the TALC model.Fourth, Lozano, Gomez, and Rey-Maquieira, in 2008, explain how the Butler TALCmodel relates to the growth theory in economics, finding evolution of tourism desti-nations to be limited by environment decline and public good availability. Fifth,Butler’s interest in the tourist resort evolvement continues, and in 2009 he questions,Cycles, waves, or wheels? Sixth, Almeida and Correia, in 2010, apply Butler’sTALC model for economic forecasting. This current research then continues byattempting to capture the S-shape of the evolvement of tourist resorts.

The Butler TALC model has been associated with economics in some of the previousresearch. Also, the development of tourism resorts has been associated with economicgrowth theory when explaining growth and decline in tourism. However, this is the firsttime an economic modeling in the form of an S-shaped polynomial is applied to capturetourism evolvement to predict when a tourism resort may reach the level of maturity.

The main implications of future research are that this current research provides apotential application of the Butler model to predict when a tourism resort may reachmaturity. Future research is likely to benefit from this current research, since it providesopportunities for better understanding of when the tourism resort reaches maturity;therefore, managers and policy planners can take advantage of this current researchwhen making strategic decisions about the development of tourism places.

This research seeks to measure a potential maximum tourism level in a range ofcountries, estimating the Butler’s tourist area cycle of evolution, by capturing the S-shape of the cycle. It allows for accountancy of both the convex U-shape relationshipand the concave >-shaped relationship. Therefore, the S-shape of the cycle makes itpossible to capture a potential top of the cycle when finding its maximum level.

First, the regression model for the cycle is estimated for a range of OECD countries;second, estimations for the small open economies of Norway and Iceland are obtained;and third, these estimates are obtained for one particular country – Iceland.

The S-shape of the cycle is applied to three measures of tourist inflow: inbound tour-ists, occupancy, and reported guest-nights. These three variables are estimated to besubject to changes in infrastructure and accounted for by paved roads and Internetaccess availability as well as other economic factors, such as government efficiency,central bank policy, and the quality of skilled labor.

Overall, results indicate that it is possible to capture the S-shape characteristics of theButler’s tourist area cycle by applying an S-shaped polynomial function. This is foundto be possible when analyzing OECD countries, Iceland and Norway separately, andIceland solely as a single country case.

All in all, the results indicate that Butler’s tourist area cycle of evolution can be esti-mated economically to forecast a potential peak in tourist arrivals to countries.

Future research implications for this field, using the Butler cycle, may suggest furtheruse of the Butler cycle when estimating the potential maximum tourism level at touristdestinations.

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Tourism industry implications include that, when the tourist level has reached acertain maximum, further development may lead to stagnation.

Tourism planning implications include that this type of application of the Butlercycle can provide opportunities in tourism planning and in determining whentourism destinations are likely to reach a maximum tourism level. This could help inthe organization of tourist resorts.

The research therefore has implications for future research and for the tourism indus-try as well as its potential application for tourism planning in the future.

Acknowledgement

I wish to thank for helpful comments by Elisa Contryman Stead and Vilborg Julıusdottir.

Disclosure statement

No potential conflict of interest was reported by the author.

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