Can the Butler's Tourist Area Cycle of Evolution Be ...

Scandinavian Journal of Hospitality and Tourism

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Can the Butler's Tourist Area Cycle of Evolution Be Applied to Find the Maximum Tourism Level? A Comparison of Norway and Iceland to Other OECD Countries Helga Kristjánsdóttir To cite this article: Helga Kristjánsdóttir (2016) Can the Butler's Tourist Area Cycle of Evolution Be Applied to Find the Maximum Tourism Level? A Comparison of Norway and Iceland to Other 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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Scandinavian Journal of Hospitality and Tourism, 2016 Vol. 16, No. 1, 61 –75, http://dx.doi.org/10.1080/15022250.2015.1064325

Can the Butler’s Tourist Area Cycle of Evolution Be Applied to Find the Maximum Tourism Level? A Comparison of Norway and Iceland to Other OECD Countries ´ NSDO ´ TTIR HELGA KRISTJA University of Iceland, Iceland

A BSTRACT This research seeks to analyze the S-shape of the Butler’s tourist area cycle of evolution in order to capture the maximum tourist level. It is the first time this type of economic 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 the cycle is applied to forecast a potential peak in inbound tourists in a particular country and sample of countries. To capture the non-monotonic relationship of the cycle, a fifth-degree polynomial is put forward, accounting for government, banks, roads, skilled labor, and Internet application. Results indicate that the S-shape of the Butler’s tourist area cycle of evolution can be captured with a polynomial function for a range of OECD countries, as well as for Norway and Iceland combined and for Iceland solely. This can be interesting as well as useful for tourism researchers seeking to explain the flow of tourists. The main implication of this study to managers and tourism policy planners is the potential to apply the TALC model to estimate development and potential peaks in the tourism industry in advance, years before the tourist level reaches maturity at the top. K EY W ORDS : 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 expanding industries in the world (OECD, 2008). The evolution of the tourism industry is described in Butler’s (1980) tourist area cycle of evolution. In his research, Butler (1980) seeks to explain the implications for management of resources, introducing the concept of a tourist area cycle of evolution, also referred to as the tourism area life cycle (TALC) model. Furthermore, in his discussion of tourism in the future, Correspondence Address: Helga Kristjańsdo´ttir, University of Iceland, Gimli, Post box 32, Sæmundargo¨tu 2, 101 Reykjavı´k, Iceland. E-mail: [email protected] # 2015 Taylor & Francis


62 H. Kristjańsdo´ttir Butler (2009) poses the following question: cycles, waves, or wheels? When referring to the shape of the curve for tourist resort evolution. Tourism is viewed as economic activity by Butler (2009) in an attempt to derive the evolvement of tourist resorts. Gilbert (1939) and Christaller (1963) present three phases in the development of tourist destinations: (1) discovery, (2) growth with rising demand, and (3) decline with decreasing demand. Manente and Pechlaner (2006) discuss the potential use of the TALC model for the predictability of the demand for tourist resorts, taking into account the economic role of tourism, among other things, in the TALC model. Also, Berry (2006) discusses whether it is possible to test the different evolvement stages of tourist resorts using the TALC model when testing the different stages of the TALC model. Lozano, Gomez, and Rey-Maquieira (2008) explain how the Butler TALC model relates to the growth theory in economics, using numerical calculations to explain the tourism pattern; they find the evolution of tourism destinations to be limited by environment decline and public good availability, eventually leading to stagnation. Also, Almeida and Correia (2010) seek to determine the connection between the Butler TALC model and economic forecasting and analyze tourism in the Spanish island Madeira using the TALC model. Their findings indicate that Madeira tourism growth, in accordance with the TALC model, cannot continue further and that the local tourism is reaching its phase of maturity. Their analysis covers the time period of 1976–2006. This current study includes a North Atlantic perspective, with a specific focus on Norway and Iceland, countries receiving attention in recent analysis by Baldacchino, Helgado´ttir, and Mykletun (2015), Sigurðardo´ttir and Helgado´ttir (2015), and Engeset 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 of the tourism industry? This research seeks to answer the question by presenting the S-shape of the cycle. The S-shape presentation is in line with Butler’s presentation of the cycle; it allows for determination of growth, first increasing slowly and then increasing more steeply until it reaches maturity at the top. The approach involves the 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 the maximum point, with an application of a fifth-degree polynomial, which accounts for the S-shape of the curve. Butler (1980) provides an extension over six phases using the tourist area cycle of evolution 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 the Butler’s (1980) tourist area cycle of evolution are exhibited in Figure 1. These stages are: (1) exploration, (2) involvement, (3) development, and (4) consolidation. These are 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 the PLC. The S-shape curve is also visible in Figure 2(a) and 2(b). The figures exhibit the business life cycle, with Figure 2(b) accounting for revenues and profits. The shape of the cycle corresponds with the shape of the Butler’s (1980) tourist area cycle of evolution.


The TALC Model and the Maximum Tourism Level 63

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

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

In the current research, economic regression estimates are obtained for a range of OECD countries, the Nordic countries of Norway and Iceland combined, and the particular 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 frequency, indicating how common a particular event is. Another common distribution, the Pareto distribution, is well known when determining the distribution of income


64 H. Kristjańsdo´ttir (Jagielski, Duczmal, & Kutner, 2015; Yang, Zhang, Liu, Li, & Wan, 2007). Also, the turning points, indicating the change from a decrease to an increase and vice versa, of the product lifecycle model have been studied (Huang & Tzeng, 2008), and regression models 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 evolution (1980) for the Balearic Islands. Also, Lundtorp and Wanhill (2001) make an attempt to formulate an explanation of the lifecycle model. Their approach to the explanation is that it is demand driven, indicating that the cycle evolvement is primarily explained 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 in Spain (Garay & Ca`noves, 2011). When explaining the evolvement stages, Butler (1980) mentions carrying capacity, land scarcity, water quality, air quality, transportation, and accommodation. Butler (1980) also refers to social factors such as crowding dislike and local knowledge. Garay and Ca`noves (2011) find the Butler cycle to account for economic as well as the territorial explanation of tourism. However, these previous studies generally do not associate the Butler’s (1980) tourist area cycle of evolution with economics, like this current research does. First, the analysis for the OECD countries is provided, and the research is extended to provide a comparison for Norway and Iceland to other OECD countries. Finally, the fact that Butler (2009) concludes that the tourist area cycle of evolution “works well with destinations established in earlier days” may suggest that the model could predict 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 fact that it experienced a 20% growth in foreign visits in the year 2012 (European Travel Commission, 2013). This is the very first time Butler’s (1980) tourist area cycle of evolution is being modeled economically to test the S-shape of the cycle and is applied to forecast when it is likely to reach a certain maximum level in the number of tourists arriving in a particular country. Also, the OECD country comparison is unique in this current research. To capture the S-shape of the Butler’s tourist area cycle of evolution, it is particularly interesting to consider how the change between phases is marked by the turning points. The turning points are points of inflection. They determine when the functional form changes from concave to convex. More specifically, the convex part presents the U-shaped relationship; however, the concave part the >-shaped relationship. Figure 3 shows the interpretation of the tourist development stages proposed by Lundtorpa and Wanhill (2001). Figure 3 shows the relative number of tourists, with the relative number 0.09 presenting 9% of the tourist maximum of 100%, proceeding to 0.21 (21%), 0.79 (79%), and 0.91 (91%). Finally, the top (upper-right) of the curve corresponds to 100%, and likewise the bottom (lower-left) to 0%. Because of the bell-shape or S-shape, the turning point is in t0, where the slope goes from being positive and increasing to being decreasing. Because of the S-shape of the curve, the



Figure 3.

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

relative numbers on each side of t0 correspond to each other, with t1 and t2 being equally distant 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 and the second derivative is also positive, indicating that the slope of the function is positive and increasing: f ′ (x) . 0

f ′′ (x) . 0.

(1)

However, in stage (4) consolidation, and stage (5) stagnation, the slope is positive but decreasing, 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 when countries are likely to reach the potential maximum point of tourists when entering into


66 H. Kristjańsdo´ttir the stagnation or maturity stages. This current research assumes that, by the end of the period, there is a decline rather than rejuvenation (Butler, 1980). This is presumed to be pessimistic rather than optimistic, since it is not possible to assume both a decline and rejuvenation. The research then continues by presenting an equation to capture the shape of the PLC. The polynomial equation is chosen for estimation, since the polynomial shape accounts 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 setup with international trade modeling. Based on Bergstrand (1985), modeling exports is the trade-dependent variable here, since tourism is one form of export, and therefore one form of international trade. Within the trade literature, it is common to analyze trade flow to one particular country, like in the case of Tekin-Koru and Waldkirch (2010), who seek to explain heavy reliance on trade by country. In the same setting, this current research focuses on 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 + b1 x1ij,s,t + b2 x2ij,s,t + b3 x3ij,s,t + b4 x4ij,s,t + b5 x5ij,s,t + 1 ij,s,t ,

(5)

where the sector notation, denoted with s, is set to account for the tourist industry specifically. Since the sector is fixed to solely account for the tourist sector, the S denotation in the dependent variable and error term is not necessary. The equation therefore becomes as found in Equation (6): touristsij,t = t0 + t1 x1ij,t + t2 x2ij,t + t3 x3ij,t + t4 x4ij,t + t5 x5ij,t + zij,t .

(6)

The export equation has therefore been narrowed down as to solely account for export 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 now solely accounts for the tourist sector. Therefore, the inflow of tourists from country i to 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 transparency of government policymaking. Therefore, the government variable is chosen for application in this current research. Furthermore, WEF accounts for the factors of prevalence of foreign ownership, property rights, and business impact of rules on FDI, and to account for these factors, the variable bank is included. Moreover, WEF incorporates factors accounting for quality of roads and road density, and therefore the paved roads variable is used here. Also, factors such as primary education enrollment, secondary education enrollment, quality of the educational system, local availability of specialized research and training services, and extent of staff training are included by WEF; therefore the variable skilled labor is applied in this research.



Furthermore, the WEF incorporates factors accounting for Information and Communications Technology, or ICT use for business-to-business transactions, Internet use for business-to-consumer transactions, individuals using Internet and broadband Internet subscribers. Therefore the variable internet is incorporated in this current research. The factors accounted for in this current research are infrastructure-related factors such as paved roads and Internet access (World Bank, 2013) and macro-economic factors such as government efficiency. Also, the central bank policy (IMD, 2012) is included 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 + g1 governmenti,t + g2 bank2i,t + g3 paved roads3i,t + g4 skilled labor4i,t + g5 internet5i,t + hij,t .

(7)

In the setting of the PLC, skilled labor is presented in the following equation: nightsij,t = k0 + k1 governmenti,t + k2 bank2i,t + k3 paved roads3i,t + k4 skilled labour4i,t + k5 internet5i,t + jij,t ,

(8)

where it is presumed that the error term 1ij is log-normally distributed with E(ln 1ij ) = 0. occupancyij,t = u0 + u1 governmenti,t + u2 bank2i,t + u3 paved roads3i,t + u4 skilled labour4i,t + u5 internet5i,t + vij,t .

(9)

Evolvement of resort areas through time has gained the attention of researchers such as Kostiainen (2007), focusing on the Northern Riviera life cycle for the Terijoki resort area close to St. Petersburg. The Terijoki area development is studied over three periods of time: first, when governed by the Russian Czar, second, then, governed by Finland authorities, and third when under Soviet, or Russian, governance. These three phases are viewed as cycles, separated by political changes or wars. Although located distant from other places, Terijoki is referred to as the Northern Rivera to underline its “dignified and attractive location.” Here the focus is on the Northern countries, Norway and Iceland, in comparison to a range of OECD countries. The sample therefore covers data on these countries. These countries are selected since they are believed to provide sufficient variation; this is reflected in the country sample selection. Variation is based on different country and population sizes. Data on the variable nights are obtained from Eurostat (2013) for the following countries: 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

68 H. Kristjańsdo´ttir Table 1.


Variable

Description

Nights Occupancy

Guest-nights Occupancy rate

Tourists

International tourists

Variable definition. Definition

Total nights spent by non-residents Net occupancy rate of bed places in hotels and similar establishments

Number of international tourists. Inbound tourists, from origin countries (i) to host country ( j), over time (t) Government Government Government decisions are effectively efficiency implemented. Index from 0 to 10 Bank Central bank Central bank policy has a positive policy impact on economic development. Index from 0 to 10 Paved roads Paved roads % of Paved roads are those surfaced with total roads 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 Skilled Skilled labor Skilled labor is readily available. labor Index from 0 to 10 Internet Internet users, per Internet users are people with access 100 people to the worldwide network

Source of data in current research Eurostat (2013) Eurostat (2013) and Statistics Iceland (2013) Icelandic Tourist Board (2012)

IMD (2012) IMD (2012)

World Bank (2013)

IMD (2012) World Bank (2013)

total nights spent in each country by non-residents in collective tourist accommodation establishments. Table 1 explains all the variables used. The variable occupancy accounts for the net occupancy 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 countries except Iceland, the data for which are obtained from Statistics Iceland (2013). Data from Eurostat (2013) cover hotels and similar establishments. However, data from Statistics Iceland (2013) only covers hotels, with hotels accounting for the vast majority of accommodation in Iceland. Data on occupancy are obtained for the following countries: Austria, Belgium, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, and the UK. Data on the dependent variable tourists span the number of tourists arriving in Iceland. Data on tourists are from the Icelandic Tourist Board (2012), running through the period of 2003–2011 and spanning over 93% or more of foreign visitors passing through Keflavik International airport every year, registering their nationalities. The sample of countries is a result of the nationalities; the countries included are the


The TALC Model and the Maximum Tourism Level 69 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, it accounts for the phrase “Government decisions are effectively implemented.” The index runs from 0 to 10 and is obtained from the IMD (2012). The bank variable accounts for central bank policy, or more specifically, “Central bank policy has a positive impact on economic development.” The index runs from 0 to 10 and is obtained from the IMD (2012). One of the things Butler (1980) addresses when discussing the TALC model is the importance of transportation; therefore, one of the variables here accounts for paved roads. The variable paved roads represents paved roads as a percentage of total roads. Paved roads are those surfaced with crushed stone (macadam) and hydrocarbon binder or bituminized agents, with concrete or with cobblestones, as a percentage of all of the country’s roads, measured in length. The variable source is received from the World Bank (2012). The World Economic Forum (2013) Travel & Tourism Competitiveness Report stresses the importance of including quality of roads, when considering ground transport infrastructure and when reporting travel and tourism competitiveness of nations. The World Bank infrastructure definition applied in this current research includes paved roads, among other infrastructure factors. Paved roads are an important part of infrastructure in countries as an indicator of the accessibility of a country and a reflection of the level of the available infrastructure. More specifically, the World Bank’s (2012) definition is “Roads, paved (% of total roads). Paved roads are those surfaced 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 available index from 0 to 10. The variable is obtained from the IMD (2012). The internet variable accounts for Internet users per 100 people. Internet users are people with access to the worldwide network. The source for these variables is the World Bank (2013). Results Figure 4 exhibits the number of inbound tourists in Iceland during the time period 1949 –2011, with a rapid increase in recent years. The figure shows that the number of tourists has been growing rapidly in recent years. Figure 5 illustrates how the growth in the number of tourists is impacted by the innovation S-curve. New product varieties result in small-S-shapes in the overall development phases. The regression results obtained from running the statistical software STATA 10 are presented in Table 2 through Table 4. The estimates obtained for the large OECD sample in Table 2 indicate that paved roads have significant positive effects on both nights and occupancy, and the Internet does not have significant effect. However, estimates for other variables are mixed, since government is estimated to have positive effects on nights, but negative effects on the occupancy rate. The bank variable is estimated to have significant negative effects on


70 H. Kristjańsdo´ttir

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

Figure 5.

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

nights but positive effects on occupancy rate. Finally, skilled labor in the source country is estimated to have significant negative effects on occupancy rate but insignificant effects on nights. The research continues by analyzing data for Iceland and Norway. Estimation results in Table 3 are obtained by running regressions for guest night stays by foreigners in the two countries as well as the occupancy rate in the two countries. Results indicate that the condition in terms of government and banks does not have significant effects on night stays or occupancy in the two countries. The variable presenting condition of roads has positive significant effects in both countries, and skills and the Internet have negative effects, although only significantly so on guest nights. Regression results for Table 4 indicate that the government in the source country of tourists has significant negative effects on tourist inflow to Iceland; however, the bank status has significantly positive effects, and so does the paved road infrastructure and

The TALC Model and the Maximum Tourism Level 71 Table 2. OECD sample estimation results. Regressors

(i) Nights

(ii) Occupancy

Government

115270.6 (0.04) 2896667.2∗∗∗ (22.85) 53.177∗∗∗ (6.87) 2494.066 (0.91) 2.001 (20.49) 2.87e+07∗∗∗ (3.23) 0.3371 108

228.856∗∗ (22.12) 5.274∗∗∗ (4.43) .0001675∗∗∗ (5.06) 2.025∗∗ (22.06) 23.75e-09 (20.51) 309.568∗∗∗ (5.80) 0.5459 101

Bank Paved roads


Skills Internet Constant R2 Obs

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) 1694.077 (0.26) 12.068∗∗∗ (22.33) 2266.876∗∗∗ (23.44) 2.0001∗∗ (21.97) 2035569∗∗∗ (5.40) 0.9985 11

2.291 (0.41) 21.088 (20.71) .001∗∗∗ (6.80) 2.002 (20.19) 21.50e-08 (21.53) 80.146 (1.35) 0.9619 15

Bank Paved roads Skills Internet Constant R2 Obs

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

72 H. Kristjańsdo´ttir Table 4.

Flow of tourists to Iceland.

Regressors

Tourists

Government

29467.783∗∗∗ (22.88) 980.671∗∗∗ (3.85) .012∗∗ (2.00) 25.058 (21.60) 3.55e206∗∗ (2.26) 27908.52∗∗∗ (2.73) 0.3870 59

Bank Paved roads


Skills Internet Constant R2 Obs Note: Robust t-statistics reported in parentheses. ∗∗∗ Significant at the 1% level. ∗∗ Significant at the 5% level. ∗ Significant at the 10% level.

the Internet. Moreover, the level of skilled labor is not estimated to have significant effects 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.

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



Discussion and Conclusion This research on tourist resorts is based on the previous contributions: first, Butler presented a study on tourist resort evolvement in 1980. Second, Manente and Pechlaner, in 2006, 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 TALC model relates to the growth theory in economics, finding evolution of tourism destinations 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’s TALC model for economic forecasting. This current research then continues by attempting to capture the S-shape of the evolvement of tourist resorts. The Butler TALC model has been associated with economics in some of the previous research. Also, the development of tourism resorts has been associated with economic growth theory when explaining growth and decline in tourism. However, this is the first time an economic modeling in the form of an S-shaped polynomial is applied to capture tourism 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 a potential application of the Butler model to predict when a tourism resort may reach maturity. Future research is likely to benefit from this current research, since it provides opportunities for better understanding of when the tourism resort reaches maturity; therefore, managers and policy planners can take advantage of this current research when making strategic decisions about the development of tourism places. This research seeks to measure a potential maximum tourism level in a range of countries, estimating the Butler’s tourist area cycle of evolution, by capturing the Sshape of the cycle. It allows for accountancy of both the convex U-shape relationship and the concave >-shaped relationship. Therefore, the S-shape of the cycle makes it possible 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 tourists, occupancy, and reported guest-nights. These three variables are estimated to be subject to changes in infrastructure and accounted for by paved roads and Internet access 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 the Butler’s tourist area cycle by applying an S-shaped polynomial function. This is found to be possible when analyzing OECD countries, Iceland and Norway separately, and Iceland solely as a single country case. All in all, the results indicate that Butler’s tourist area cycle of evolution can be estimated economically to forecast a potential peak in tourist arrivals to countries. Future research implications for this field, using the Butler cycle, may suggest further use of the Butler cycle when estimating the potential maximum tourism level at tourist destinations.

74 H. Kristjańsdo´ttir Tourism industry implications include that, when the tourist level has reached a certain maximum, further development may lead to stagnation. Tourism planning implications include that this type of application of the Butler cycle can provide opportunities in tourism planning and in determining when tourism destinations are likely to reach a maximum tourism level. This could help in the organization of tourist resorts. The research therefore has implications for future research and for the tourism industry 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 Ju´lıúsdo´ttir. Disclosure statement No potential conflict of interest was reported by the author.

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