Longitudinal analysis of visitors’ evaluations of a World Heritage site: A comparison of five-year models from Mt. Yoshino, Japan
Yoshimi Kunieda PhD, Professor Vice President Tourism Department Osaka Seikei College Email:
[email protected]
INTRODUCTION Heritage tourism is an expanding market. Nevertheless, traffic congestion, garbage disposal, and human waste disposal are common challenges in mountainous destinations during high seasons. Mt. Yoshino, one of Japan’s World Cultural Heritage sites, is no exception. To address such challenges, major upgrades to the site may be required to increase accessibility, parking, and visitor information to improve the quality of the visitors’ experience, thus fostering sustainable tourism. Kunieda, Brigand and Guégan (2014) suggested that the need to control tourist flow to reduce the impact on natural and cultural sites and to improve the quality of the product must be recognized. Sustainable development cases following these principles exist in France; La Pointe du Raz, Brittany, and the sustainable project of Mont- Saint-Michel, are all mentioned in previous literature. By contrast, in Japan, implementing sustainable development has been challenging at Mt. Yoshino because of the surrounding area’s aging population. Stakeholders nevertheless implemented a progressive approach in 2004 to promote sustainable tourism. These stakeholders included the town; the association, the prefectural government, and the Ministry of Land, Infrastructure, and Transportation. Work on improvements began in 1994, and the new approach was adopted in 2004, however, from 2008 to 2010, congestion occurred again. This study examines the quality of the tourism product in Mt. Yoshino from 2011 through 2015 by investigating visitors’ evaluations of the destination over time, including their expectations, satisfaction, and intention to recommend or revisit the site. Mt. YOSHINO Figure 1-2 shows the location of Mt. Yoshino. It is approximately 850 meters above sea level in the town of Yoshino, in Nara Prefecture, which is the ancient capital of Japan. It is certified as one of the sacred sites and pilgrimage routes in the Kii Mountain Range and was named a World Cultural Heritage site in 2004. Figure 1 Mt. Yoshino
Figure 2 Congestion area, the entrance of Mt. Yoshino
Source: Figure 1: Geospatial Information Authority of Japan, Ministry of Land, Infrastructure, Transport and Tourism Government of Japan
The mountain has long been known for its cherry blossoms, with approximately 200 species of cherry trees and a total of nearly 30,000 trees. During the season, it is also referred to as the “thousand glances” for all the required glances under, in, on, and behind the mountain to ensure full enjoyment of the blossoms during their blossoming season in April. In recent years, the annual number of tourists visiting Mt. Yoshino has been between 800,000 and 1 million. The three weeks of cherry blossom season account for nearly half of these (300,000 to 400,000 each season) visitors (Kunieda 2013). Although the peripheral road traffic regulations are enforced, heavy congestion still occurs during the time of full bloom. The website information on blossom viewing tends to focus on visiting time, however, even though visitors try to take buses or cable cars or even walk up the hill, long queues persist in front of the Yoshino Station of the Kintetsu railway terminal. To reduce congestion, from 1994, the Mt. Yoshino Tourism Association and the town of Yoshino played central roles in implementing the operation of a shuttle bus to control the flow of visitors, particularly those traveling by private cars. Instead of implementing such a program, in 2004, the town of Yoshino applied for a “public transport activation comprehensive program” supported by the Ministry of Land, Infrastructure, and Transport. At that time, an increase in tourism was expected because of its recent designation as a World Heritage site. The program included expanding parking capacity from two areas to four, improving the shuttle bus service, implementing a charge for environmental protection, and launching a tour bus reservation system during the cherry blossom season. According to the 2005 survey, approximately 37% of the visitors were willing to pay the charge, and approximately 56% replied that they “can’t help paying.” At that time, this was a progressive approach as visitors showed an understanding of the need to avoid congestion and protect the environment. The charge is supposed to be voluntary for railway users, 1,500 yen for private car users, and 10,000–15,000 yen for tour buses. The funds support the operation of shuttle buses, parking facilities for private cars and tour buses, and the conservation and management of the environment and the cherry trees of Mt. Yoshino. The tour bus reservation system has worked well, traffic congestion has dramatically decreased, and mobility has improved. Another initiative appealed to visitors to take their garbage with them and to eliminate all the trash cans from the mountain. This seems to have resolved previous garbage problems. However, from 2008 to 2010, congestion occurred in some sections of the national highway. Despite their efforts, the management still faces challenges for maintaining sustainable development. This study commenced in 2011 to investigate visitors’ evaluations of the products in Mt. Yoshino for future tourism planning. LITERATURE REVIEW This study examines the quality of the tourism product in Mt. Yoshino from 2011 until 2015 by investigating visitors’ evaluations of the destination over time. Since tourism is a complex phenomenon, it is difficult to capture all its components in one brief description as descriptions of tourism products by researchers have long varied. Tourism is usually considered the supply side of the tourism system and comprises destination characteristics or features as well as tourism industries, services, and infrastructures at the destination. Butler (2006a) suggested that, even if not fully appreciated in many tourist destinations, resorts are essentially products; that is, they are typically developed and modified to meet the needs of specific markets (tourists) in the same way other goods and services are produced. Middleton et al. (2009) noted that many heritage attractions are part of the local character and that local specialties may be considered travel and tourism products. They suggested that the product is the visitor experience provided by the individual components (such as facilities, etc.) of the destination. Briggs (2001) also argued that, in the tourism industry, the products might be better regarded as experiences. Weaver and Lawton (2010) defined the tourism product as a combination of tourist attractions and the tourism industry, with its main components being “ease of access,” “attractiveness of the destination and the environment,” “facilities and services,” “price to the customer,” and “image of the destination.” In terms of these perspectives, the product may be defined as a bundle or package of tangible and intangible components, based on activity at a
destination. In that sense, it will be useful to understand how visitors evaluate the quality of such products at destinations and how this impacts on whether they visit again in the future. As Valles (2001) demonstrated in the final report of a working group of the World Tourism Organization that “the definition of Quality for the tourism sector must refer to the satisfaction of the consumer, in this case the tourist…Quality is the perception by the tourist of the extent to which his expectations of the product are met by his experience of the tourist product, and therefore, the satisfaction of all stakeholders is essential: the service personnel, service organizations, shareholders, the staff dealing with tourists.” In addition, the degree of tourists’ loyalty to a destination is reflected in their intentions to revisit the destination and in their recommendations to others (Oppermann 2000). Parasuraman, Zeithaml, and Berry (1994) claimed that a customer’s overall satisfaction may be related to their assessment of not only service quality but also product features and price. Therefore, this study analyzes tourism “products” through visitors’ expectations, satisfaction, revisit intentions, and intention to recommend the destination to others. The measurement scale in this research follows a five-point scale applied for most previous satisfaction-related research (Pizam et al., 1978; Tribe and Snaith, 1998) -and explores ways to identify how visitors assessed their activities at Mt. Yoshino for five years from 2011 through 2015 by utilizing multiple regressions, structural equation modeling, and multiple group structural equation modeling. METHOD Data collection The first round of primary data collection took place from the first through third weekends in April 2011. We utilized a survey questionnaire administered at three different locations—in front of the Yoshino Station of Kinki Nihon Railway, at the cable car station, and at the tour bus park area. The data collection continued similarly for five years, until 2015 (excluding a few dates due to rain). Table 1 shows the total responses from 2011 to 2015. In 2015, the number of samples decreased due to rain. Table 1 Total response for five years Total response Valid response Response rate
2011 368 352 95.7
2012 211 201 95.3
2013 165 158 95.8
2014 187 182 97.3
2015 95 95 95.7
Targets for the survey were visitors to Mt. Yoshino, whether for blossom viewing, hiking, relaxing, or visiting historical shrines and temples. Visitors completed questionnaires in a self-administered manner following their walks. The questionnaire comprised three sections designed to measure visitors’ evaluations of experiences and expectations, satisfaction, and intentions to recommend or revisit the site following their evaluation of each activity. The first section of the survey asked how the visitors obtained their information of the destination. The second section included six questions on their perceptions of their visits. These questions were responded to on a five-point Likert Scale, and possible answers were “definitely disagree,” “slightly disagree,” “yes or no,” “slightly agree,” and “mostly agree.” Twelve questions covered their evaluation of site activities; possible answers were “did not experience,” “mostly unsatisfied,” “slightly unsatisfied,” “neither satisfied nor unsatisfied,” “slightly satisfied,” and “mostly satisfied.” The final part covered the visitors’ demographic characteristics, including the number of hours and nights spent at the location, frequency of visits, and member of a party. The results are presented in Table 2 below:
Table 2 Sociodemographic characteristics of visitors 2011
2012
2013
2014
2015
a. Age 1 Under 20's 2 20's 3 30’s 4 40's 5 50's 6 above 60's Total non-responder
ｎ 6 58 33 45 46 124 312 8
% 1.9 18.6 10.6 14.4 14.7 39.7 100.0
ｎ 1 12 29 25 38 100 205 6
% 0.5 5.9 14.1 12.2 18.5 48.8 100.0
ｎ 4 3 23 13 29 87 159 6
% 2.5 1.9 14.5 8.2 18.2 54.7 100.0
ｎ 7 23 23 21 44 57 175 12
% 4.0 13.1 13.1 12.0 25.1 32.6 100.0
ｎ 3 11 16 12 17 30 89 9
% 3.4 12.4 18.0 13.5 19.1 33.7 100.0
b.Sex 1 Male 2 Female Total non-responder
ｎ 112 199 311 9
% 36.0 64.0 100.0
ｎ 81 120 201 10
% 40.3 59.7 100.0
ｎ 68 90 158 7
% 43.0 57.0 100.0
ｎ 80 102 182 5
% 44.0 56.0 100.0
ｎ 38 57 95 3
% 40.0 60.0 100.0
c.Residence 1 Osaka area 2 Tokyo area 3 Hokkaido,Tohoku 4 Chubu area 5 Hokuriku area 6 Chugoku area 7 Shikoku area 8 Kyushu area Total non-responder
ｎ 167 22 1 43 19 37 14 9 312 8
% 53.5 7.1 0.3 13.8 6.1 11.9 4.5 2.9 100.0
ｎ 102 48 1 11 2 7 1 2 174 37
% 58.6 27.6 0.6 6.3 1.1 4.0 0.6 1.1 100.0
ｎ 72 26 5 5 4 4 7 6 129 36
% 55.8 20.2 3.9 3.9 3.1 3.1 5.4 4.7 100.0
ｎ 119 18 1 8 0 6 0 5 157 30
% 75.8 11.5 0.6 5.1 0.0 3.8 0.0 3.2 100.0
ｎ 55 7 0 9 5 2 1 2 81 17
% 67.9 8.6 0.0 11.1 6.2 2.5 1.2 2.5 100.0
d.Company 1 None 2 Married couple 3 Couple 4 Family 5 Friends 6 Group Total non-responder
ｎ 10 94 26 54 98 25 307 13
% 3.3 30.6 8.5 17.6 31.9 8.1 100.0
ｎ 9 78 14 49 40 10 200 11
% 4.5 39.0 7.0 24.5 20.0 5.0 100.0
ｎ 8 53 8 34 37 21 161 4
% 5.0 32.9 5.0 21.1 23.0 13.0 100.0
ｎ 14 51 9 33 58 18 183 4
% 7.7 27.9 4.9 18.0 31.7 9.8 100.0
ｎ 4 36 9 20 18 10 97 1
% 4.1 37.1 9.3 20.6 18.6 10.3 100.0
e. Frequency of visit 1 First time 2 About 2-3times 3 About 3-4times 4 More than 5times Total non-responder
ｎ 209 67 15 23 314 6
% 66.6 21.3 4.8 7.3 100.0
ｎ 115 60 13 18 206 5
% 55.8 29.1 6.3 8.7 100.0
ｎ 90 40 11 21 162 3
% 55.6 24.7 6.8 13.0 100.0
ｎ 112 39 15 20 186 1
% 60.2 21.0 8.1 10.8 100.0
ｎ 61 18 11 7 97 1
% 62.9 18.6 11.3 7.2 100.0
f. Means of access 1 Kintetsu Railways 2 JR Railways 3 Private car 4 Tour bus Railways and 5 private car 6 Railways and bus 7 Others Total non-responder
ｎ 102 18 38 159
% 32.5 5.7 12.1 50.6
ｎ 144 25 12 36
% 69.9 12.1 5.8 17.5
ｎ 97 13 18 45
% 59.9 8.0 11.1 27.8
ｎ 151 16 7 25
% 81.2 8.6 3.8 13.4
ｎ 66 9 6 25
% 67.3 9.2 6.1 25.5
4
1.3
3
1.5
1
0.6
0
0.0
2
2.0
8 5 314 6
2.5 1.6 100.0
13 5 206 5
6.3 2.4 100.0
4 2 162 3
2.5 1.2 100.0
5 2 186 1
2.7 1.1 100.0
2 1 98 0
2.0 1.0 100.0
g. Itinerary 1 Day-tripper 2 Overnight 3 Two nights 4 More than three Total non-responder
ｎ 250 55 8 2 315 5
% 79.4 17.5 2.5 0.6 100.0
ｎ 125 56 12 5 198 13
% 63.1 28.3 6.1 2.5 100.0
ｎ 101 50 10 2 163 2
% 62.0 30.7 6.1 1.2 100.0
ｎ 160 15 11 0 186 1
% 86.0 8.1 5.9 0.0 100.0
ｎ 84 10 3 1 98 0
% 85.7 10.2 3.1 1.0 100.0
m.Type of travel 1 Private 2 Parcial package 3 Full package tour 4 Others Total non-responder
ｎ 140 28 108 28 304 16
% 46.1 9.2 35.5 9.2 100.0
ｎ 122 15 47 20 204 7
% 59.8 7.4 23.0 9.8 100.0
ｎ 60 15 40 18 133 32
% 45.1 11.3 30.1 13.5 100.0
ｎ 128 12 12 11 163 24
% 78.5 7.4 7.4 6.7 100.0
ｎ 61 5 18 4 88 10
% 69.3 5.7 20.5 4.5 100.0
Research design Many company corporate reports provide a five-or ten-year record and this can help us in construction longitudinal analysis (Evans 2015). A simple ways of assessing of a destination would be to compare the data for two or more years and what parameter, independently from each other, has increased and decreased over that time period. In this survey, a 2 step new approach is used. First, an initial scan of the figures was conducted to identify any major change overtime and to determine the main cause of negative factors that affected visitors’ satisfaction. Therefore, regression was employed for that purpose. Second, in order to identify the construct of the model for each of the five years and key factors affecting visitors’ satisfaction and intention to revisit, the research design employed factor analysis, and structural equation modeling (SEM) using simultaneous analysis of several groups by IBM/SPSS22 (2015) and Amos 22. Five year longitudinal analysis is not so common in tourism study, however, this research utilized these tools to conduct a relevant analysis of the trend of tourist behavior and perception after the sustainable program in Mt. Yoshino. Before conducting the SEM analysis, data was carefully screened and eliminated from the samples owing to some missing values. Figure 1 depicts the hypothetical causal model. Each component of the model was selected on the basis of the literature review. Visitor experience comprised activities such as visiting historical sites, cherry-blossom viewing, using restaurants and gift shops at the destination and the information, guides, parking, and rest houses provided by the destination. By comparing the five sets of responses from 2011 to 2015, this study clarifies how the constructs of each year’s model changed. The proposed model for Mt. Yoshino was used to test the hypothesis described in the following section: Figure 1 The hypothetical causal model in Mt. Yoshino H5 H3 H2 Restaurant products
Visiting historical sites
Cherry blossom viewing
Expectation
H6 H1
Quality of gifts & service
Intention of revisit
Satifsaction
H4
H7
Intention of recommendation
Hypothesis The following hypothesis were set and tested based on the theoretical relations among valuables. Normally, visitors’ perceptions of service quality in restaurants and souvenir shops selling local products affect visitors’ experience, therefore, H1: The quality of gifts and services will affect restaurant products. H2: Restaurant products and gift quality will affect visitor experiences when visiting historical sites. Visitor experience is often related to cherry-blossom viewing, therefore, H3: Visitor experience visiting historical sites will affect their perception of cherry-blossom viewing. If visiting World Cultural Heritage sites for their historic value is satisfying for visitors, they will recommend the sites by word of mouth. H4: Visiting historical sites will positively impact intentions to recommend them. If cherry-blossom viewing is assumed to be their main purpose for the visit, then, H5: Cherry-blossom viewing will positively impact visitors’ expectations. If blossom viewing was better than expected,
H6: Blossom viewing will positively impact their satisfaction. H7: Blossom viewing will positively impact their intention to revisit. RESULTS Results, Regression, and Analysis of Variance (ANOVA) First, to identify any changes in the variables over the five years, we calculated the number of those who responded “somewhat satisfied” and “highly satisfied,” excluding those who responded, “did not experience.” Table 3 shows the changes over time in means of expectation, satisfaction, intention to revisit, and intention of recommendation (word of mouth) from 2011 to 2015, along with each season’s precipitation. It shows that precipitation seems negatively co-related with satisfaction while satisfaction, recommendation and revisit are co-related and expectation slightly declines. Thus the weather seems to significantly affect visitors’ satisfaction at such destinations. Utilizing regression analysis, the results showed that both the relationship between the mean value of satisfaction and intention to recommend and intention to revisit were positively correlated; the correlation between satisfaction and intention to recommend was y = 2.6236 × −6.5334, r2 = 0.8112, and between satisfaction and intention to revisit: y = 2.7939 × −7.6992, r2 = 0.8114. On the other hand, precipitation and satisfaction were negatively correlated, suggesting that satisfaction strongly correlated with good weather. In addition, expectation was negatively correlated with satisfaction (y = −0.6175x + 6.1791, r 2 = 0.2034). This seems to indicate that those who have lower expectations tend to feel more satisfied with their experience and vice versa. Table 3 Rating trends and precipitation from 2011 to 2015
In the second step, we used a one-way analysis of variance(ANOVA) with a post-hoc test (Bonferroni) to determine whether the mean value of common visitor experience variables in Mt. Yoshino over five years differed significantly by demographic characteristics, number of hours or nights spent at the destination, time of visit, travel companions, or means of travel. The findings after ANOVA are as follows: 1. Repeat visitors and those who stayed longer were more satisfied than other visitors. 2. Repeat visitors from the Osaka and Tokyo metropolitan districts were more satisfied than others. 3. Railway users were more satisfied than tour bus users. 4. Satisfaction tends to diminish with visitor age.
Results and Covariance Structure Modeling For the third step, Exploratory Factor Analysis (EFA) was conducted using maximum likelihood estimation with promax rotation. All the factor loadings were greater than 0.45. Bartlett’s test confirmed the strength of the relationship among variables. This tested the null hypothesis that the correlation matrix is an identity matrix. From the same table, the Bartlett’s Test of Sphericity was significant; its associated probability was less than 0.05 (0.012, which is small enough to reject the null hypothesis). The same procedures were repeated for the data sets from 2012 to 2015. Multiple-group analysis of Structural Equation Modelling was utilized to compare not only the relationships among factors but also the structures of entire factors. First, based on the hypothesis, the five models from 2011 to 2015 were developed to estimate the parameters of each model and were then tested and compared. The criteria based on the following conditions were used to compare the models: 1) The estimate was accurately made. 2) Path coefficients (standardized coefficients) and goodness of fit index were compared. 3) The Heywood case was excluded because of an unsuitable solution. 4) Results from the models should be easy to interpret and useful. The results are shown in Table 4 and the best-fitting model is shown in Figure 2. The goodness-of-fit test statistics are shown in Table 4, which indicates the path coefficients and goodness-of-fit indicators of the hypothesis model of each year from 2011 to 2015. The overall construct of model fit indices was good and common structures (except for 2012 and 2013) are indicated in the shaded area. Products of restaurants and visiting historical sites were negatively correlated with intentions to revisit from 2011 through 2013. Relations between blossom viewing and satisfaction were stable, and blossom viewing had a highly positive impact on expectation. On the other hand, the path coefficient from satisfaction to word of mouth (intention of recommendation) tended to decrease. Table 4 Comparisons of the five models Latent valuables
2011 Model
2012 Model
2013 Model
2014 Model
2015 Model
0.74
0.42
0.46
0.71
0.40
0.60
0.11
0.47
0.49
0.32
quality of gifts
↓ restaurant products
↓ Path coefficiance
visiting historic sites
↓
↓
blossom viewing
↓ expectation
0.66
-0.04
0.2
-0.03
0.36
-0.01
0.36
0.28
0.51
0.19
0.59
0.47
0.57
0.28
0.38
0.05
0.38
0.40
0.65
0.41
revisit
↓ satisfaction
↓
0.37
0.51
0.35
0.42
0.40
satisfaction
↓ revisit
↓
0.5
0.46
0.46
0.45
0.43
0.45
0.42
0.29
0.42
0.29
recommendation
↓
0.32
0.27
0.45
0.50
0.22
0.946 0.913 0.987 120.574
0.910 0.854 0.957 139.544
0.876 0.799 0.909 155.108
0.945 0.911 0.994 112.082
0.885 0.806 0.95 107.745
recommendation
Goodness of Fit
GFI AGFI CFI AIC
* The Goodness of Fit Index (GFI), Adjusted Goodness of Fit Index (AGFI), and Comparative Fit Index (CFI) should be larger than 0.9 to reflect a good fit. Akaike Information Criterion (AIC) suggests a test of relative model fit. The preferred model is the one with the lowest AIC value.
Figure 2 presents the construct of the 2014 model. The overall model fit for 2014 was good and is the most similar to the hypothesis model. The 2011 model, with one different path coefficient, was also close to the 2014 model. However, in the 2012 model, satisfaction with price and local hospitality affected impressions of product quality, and in 2013, the quality of products positively affected impressions of blossom viewing. Consequently, visitor satisfaction strongly impacted not only impressions of restaurant and gift shop services and prices but also their impressions of products quality. Thus, if quality declines, word-of-mouth publicity may lose its credibility. Figure 2 2014 Hypothesis Model
The results of the hypothesis testing are indicated in Table 5. Table 5 Results of hypothesis test Hypothesis proposed in this study
Rsults
H1: Quality of gifts and service affect restaurant products. H2: Restaurant products and Quality of gifts affect visitors’ experiences; visiting historical sites. H3: Visitors’ experiences; visiting historical sites affect perception toward cherry blossom viewing. H4: Visiting historical sites affect positively their intention of recommendation. H5: Cherry blossom viewing positively affects visitors’ expectation. H6: Blossom viewing affects positively their satisfaction. H7: Blossom viewing affects positively their intention of revisit.
supported not supported not supported not supported supported supported supported
CONCLUSION This study demonstrates unique tools as longitudinal analysis to clarify the trend of visitors’ evaluation in Mt. Yoshino for five years after the traffic congestion occurred again in 2008-2010. In conclusion, it is encouraging that the five-year analysis produced meaningful results for Mt. Yoshino so that they can be shared with the town of Yoshino and the management organization for Mt. Yoshino tourism. The study’s findings indicate the following: first, weather seems negatively affects visitors’ satisfaction; in this case, precipitation may lead to disappointment for five years, which is a common factor for destination with natural attractions. Today the
long-term weather forecast may help the destination management to avoid some negative impact for the season to come. Second, the common factor related with expectation and satisfaction is “cherry blossom viewing” which is the core product of this destination and should be maintained properly. Third, the mean value of the quality of service at restaurants and souvenir shops, toilets, and rest houses indicate low rating during that period. Both service and infrastructures are needed to be improved. In particular, service and personal contacts with visitors at restaurants and gift shops are key indicators of quality; therefore, tourism industries must provide opportunities for regular employee training to have good communication skills among employees as well as professional service. Fourth, closeness with local people shows negative rating. Lastly revisits show relation with “relaxed” and “feel with nature,” which means that it is still significant to control the visitors’ flow for them to feel relaxed and enjoy nature. The factors that caused dissatisfaction are age. Elderly people tend to be less satisfied and so are visitors coming by bus; on the other hand, families are more satisfied than couples traveling by railways. For those who stayed longer, city dwellers are more satisfied than visitors from the countryside. Repeat visitors are also from big cities and are satisfied visitors. Overall tendency after five years suggests a trend toward improvement. It is significant to create quality tours by bus for elderly visitors, in addition, positive attitudes of residents toward the tourism business key factors to communicate with visitors and express hospitality. Protection of the thousands of cherry trees is taken for granted by the tourism management but hospitality and service quality in the whole Yoshino Town should be well provided for future tourism planning. This research has a number of limitations including an unstable collection of samples due to variable weather and conditions. However, the research models and their associated measures proposed in this study provide interesting ideas and scope for developing future work and sustainable tourism mechanisms in mountainous areas. The research is still in progress but will be developed to construct a framework for how the stakeholders at the destination can work together toward sustainable tourism. ACKNOWLEDGEMENTS This study would not have been possible without the generous support of Yoshino Town, Mt. Yoshino tourism association and Professor Shoji Yamamoto, Kwansei Gakuin University. This study was supported by JSPS Grants-in-Aid for Scientific Research Grant Number 60465870. REFERENCES Briggs, Susan (2001). Successful Tourism Marketing, Kogan Page Limited: London. Butler, Richard W. (2006a) The Origins of the Tourism Area Life Cycle. In Butler, R.W. (Eds.), The Tourism Area Life Cycle Volume 1 Applications and Modifications, p. 13–26, Clevedon: Channelview Publications. http://books.google.co.uk/books?id=XHTxrqnn9sMC&pg=PR3&dq=The+Tourism+Area+Life+Cycle+V olume+1+Applications+and+Modifications&hl=en&ei=CuVvTOSiK4el4Qbh-cGbCw&sa=X&oi=book_r esult&ct=result&resnum=1&ved=0CCgQ6AEwAA#v=onepage&q&f=false Evans, Nigel (2015) Strategic management for tourism, hospitality and events, 2nd edition, Routledge. Kunieda, Yoshimi(2013)Environmental protection and tourism marketing; A study of regional alliance in Japan and overseas, Graduate School of Institute of Business and Accounting, Kwansei Gakuin University Repository. Kunieda, Yoshimi & Louis Brigand, Cécile Guégan (2014). Perceptions of sustainable tourism in Mont-Saint-Michel: Japanese tourist attitudes after introduction of the new transportation system, Travel & Tourism Research Association 2014 International Conference, Brugge, Belgium, 350-367.
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