Inbound tourism in Thailand: Market form and scale differentiation in ASEAN source countries

Tourism Management 64 (2018) 22e36

Contents lists available at ScienceDirect

Tourism Management journal homepage: www.elsevier.com/locate/tourman

Inbound tourism in Thailand: Market form and scale differentiation in ASEAN source countries Yaping Liu a, b, *, Yinchang Li b, Parnpree Parkpian b a

China-Asean Research Institute of Guangxi University, No.100 Daxue Rd., Nanning, Guangxi Zhuang Autonomous Region, 530004 China Department of Tourism Management, Business School, Guangxi University, No.100 Daxue Rd., Nanning, Guangxi Zhuang Autonomous Region, 530004 China

b

h i g h l i g h t s
Present a 20-year trend and change trace of tourism from ASEAN countries to Thailand.
Boston consulting group matrix (BCG) is used to analyze the market form over 20 years.
Use Primacy Index, Gini coefficient, and Herfindahl-Hirschman Index etc in the analysis.
Some names of four quadrants have been appropriately modified.
Propose a two-dimensional structure pattern to discuss the impact of inbound tourists.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 17 October 2016 Received in revised form 23 July 2017 Accepted 24 July 2017

Utilizing Thailand's inbound tourism statistics for the period 1996 to 2015, we focus on the change and differentiation of its inbound tourism sources from the ASEAN countries. The results are as follows: first, the number of tourists to Thailand from ASEAN countries and Thailand's foreign exchange earnings saw an average growth rate of more than 10%. Second, we find that only few ASEAN source countries are star markets, while most of them belong to the dog or child market categories. Third, by employing the distance decay pattern, we highlight a significant Boltzman curve between inbound tourists and travel distance; moreover, bilateral trade emerges as an important factor for Thailand's inbound tourist flows. Fourth, using five indicators, we investigate scale differentiation within these countries, and find a Primacy Index value greater than 2, suggesting that although the market seemed highly concentrated, a balanced development trend is apparent. © 2017 Published by Elsevier Ltd.

Keywords: Inbound tourism Market form Scale differentiation Distance decay Thailand ASEAN source countries

1. Introduction Thailand is one of the most attractive tourism destinations within the Association of Southeast Asian Nations (ASEAN). The association began the development of its tourism industry in the 1960s. With rapid growth in the number of inbound tourists, Thailand was ranked fourth in the world in 2015 in inbound tourism, and Bangkok, its capital, was ranked second among global tourist destinations in the number of overnight tourists. The

* Corresponding author. Department of Tourism Management, Business School, Guangxi University, No.100 Daxue Rd., Nanning, Guangxi Zhuang Autonomous Region, 530004 China. E-mail addresses: [email protected] (Y. Liu), [email protected] (Y. Li), [email protected] (P. Parkpian). http://dx.doi.org/10.1016/j.tourman.2017.07.016 0261-5177/© 2017 Published by Elsevier Ltd.

tourism industry has become the second largest pillar of Thailand's economy. The establishment of the ASEAN Economic Community (AEC) in 2015 has provided Thailand's inbound tourism market greater development opportunities; however, it has also led to more pressure from competition. Compared with other ASEAN countries, Thailand may have advantages in terms of geographical location, tourism resources, and cultural background. However, owing to strong development in the tourism industry of Singapore, Malaysia, and Cambodia, which have homogeneous substitutive tourism products, Thailand's inbound tourism market is facing increasing competition. This claim can be further substantiated with statistics from the “Travel & Tourism Competitiveness Report 2015” released by the World Economic Forum (WEF), which shows the tourism competitiveness rankings of 141 countries, with Singapore coming in at

Y. Liu et al. / Tourism Management 64 (2018) 22e36

11th and Thailand rising from 43rd in 2013 to 35th in 2015 (WEF., 2015; 2013). Although Thailand's ranking has improved by 8 places, other ASEAN countries have also made significant improvements, with Malaysia improving by 9 places since 2013 to take the 25th position. Furthermore, Indonesia sharply narrowed its gap with Thailand, improving by 20 places to rank 50th. In developing the tourism industry, other ASEAN countries have been constantly developing new tourism products. As the financial center and sea transportation hub of ASEAN, Singapore has established new and innovative tourism resource mechanisms, with projects such as Gardens by the Bay, F1 Night Race, and international expositions, to continue enhancing its tourism resources and tourist experiences. As Thailand's strong competitor in tourism development, Malaysia has proposed the “Malaysia Tourism Transformation Plan” (MTTP) to promote domestic tourism. The tourism industry of Laos and Cambodia has also been developing rapidly in recent years. Both countries offer very similar tourism resources and products as Thailand, which will further affect the competitiveness and growth of the latter. According to the Thai Royal Immigration Bureau statistics, although tourists from the Chinese mainland accounted for 21.47% of Thailand's total inbound tourists, the other nine ASEAN countries accounted for 22.45%. In addition, the remaining East Asian countries such as South Korea and Japan accounted for 5.86% and 4.67% respectively, while Hong Kong and Taiwan accounted for only 1.37% and 1.79% respectively. Thus, it is observed that the ASEAN countries (as a whole) account for a significantly high proportion of Thailand's inbound tourists, approximately over 20% of the total. This shows that it is necessary to undertake a separate analysis on the market structure of inbound tourists' source countries for each of the nine ASEAN nations. Hence, maintaining its advantageous position among AEC members in this new era, and avoiding negative impacts on the tourism industry, caused by factors such as political turmoil, has emerged as a major challenge for its tourism industry (WEF., 2015). Therefore, based on the national tourism statistics of ASEAN countries during 1996e2015, this study intends to analyze the market forms and scale differentiation of Thailand's inbound tourism (source countries) to uncover the market form variations in these countries, explore the development potential of Thailand's tourism industry, and provide a reference to expand its inbound market and improve competitiveness. 2. Literature review and methodology 2.1. Literature review Quantitative research on inbound tourism market can be traced back to 1927, when A. Mariotti of Sapienza University of Rome conducted a quantitative analysis on tourism flow from an economics perspective. Since, studies on tourism flow have received extensive attention (Coshall, 2000; Gonzalez, 2011; Mei, 2014; Shen, 1999). Charles, Goeldner and Brent (1999) introduced the shift-share method (SSM), a practice used in regional economic analysis, to study the changes and trends of the tourism market, dividing the market into four types: rising, thriving, ceasing, and fading (Charles et al., 1999; Lü, Wang, Gong, & Cheng, 2006). Following this development, many scholars began applying the SSM to analyze the structure of the tourism market. Seung-Mook (2015) investigated the competitiveness of the tourism market in
th, D
South Korea, China, and Japan through the SSM; To avid, and Vasa (2014) used it to explore the influence of travel distance on the tourism industry in Europe. Wang and Gao (2008) and Fan, Yan, and Shi (2009) adopted the method to examine inbound tourism in Shanghai and Gansu provinces. In addition to the SSM, Chinese

23

scholar Li and Sun (2002) introduced the BCG matrix into the study of tourism markets, and proposed the tourism market competition model. Liu and Gao (2007), Zhang and Zhang (2007), and Fang and Deng (2014) applied a preference scale and a competitive situation analysis as indicators to investigate markets with different sources of tourism. In addition to the above studies on the tourism market structure, other relevant research topics are the factors influencing tourism streams and the analysis of the market laws (Wray, 2015; Wang, Tian, Lu, Wang, & Lew, 2015; Tang, Wu, Wang, & Yang, 2012; Liu et al., 2012). Shi, Zhou, and Shen (2014) point out that China's GDP, transportation and tourism resources quality, as well as total foreign trade represent the main influential factors of inbound tourism for the destination. In the empirical study, the scholar also detected a “distance decay” phenomenon in the tourist stream (Hooper, 2015; Wu and Bao, 2015). Lee, Guillet, Law, and Leung (2012) highlight the presence of a distance decay pattern with two secondary peaks depending on the distance to the chosen destination from Hong Kong. In terms of methodology, although complex measurement models are widely employed by scholars studying market structure and laws, the more visual and concise graphic structure analysis has also gained popularity. For example, Liu, Chen, Zhu, and et al (2016) and Racherla and Hu, (2010) empirically study the market structure of different destinations by employing social network analysis, while the travel accessibility of a certain destination in Denmark is investigated by SkovPetersen (2001) through a distance decay pattern drawing the two-dimensional structure graph. At the same time, scholars have adopted other statistical indicators to analyze the degree of differentiation in the scale of tourism markets. Weng (2008) and Liu and Ma (2012) applied a standard deviation (SD) method and the Gini coefficient (G) to analyze the degree of differentiation in the development of tourism markets of different cities. Wang, Zhang, Shi, and Wang (2013) employed a location quotient (LQ) and the Theil index to explore the degree of market concentration and differentiation in the island counties of China. Liu et al. (2016)applied a 2-mode internet analysis (social internet analysis) to probe the inbound market structure among China's provinces. It is a newer method. Despite these developments, studies conducted by scholars both home and abroad still need continuous improvement. Research methods require improvement and innovation, and research on the dynamic share-shifting of the market has become a hot topic, which needs in-depth investigation. Therefore, we managed to combine Market Form of Tourism System and a two-dimensional structure pattern with other indicators such as Primacy Index, Gini coefficient, and Herfindahl-Hirschman Index etc to explore the dynamic changes in inbound tourism in Thailand. 2.2. Methodology 2.2.1. Market form of tourism systems When analyzing the distribution of Thailand's inbound tourism from other ASEAN countries, we adopted the tourism market form that was named as tourism market competition model in a study by Li and Sun (2002). The model is derived from the BCG (Boston Consulting Group, BCG) Matrix, which is used to analyze the development strategy of a company (Myllyl€ a & Kaivo-Oja, 2015). However, we intended to focus only on the classification of market forms rather than competitiveness among ASEAN countries. Therefore, we named the model “the market form of tourism systems,” which adopts both market share (a) and growth rate (b) of players in the tourism sector as indicators of their market features, marked as U (a,b) (Li & Sun, 2002). The market form of tourism systems is a market analysis method developed on the basis of

24

Y. Liu et al. / Tourism Management 64 (2018) 22e36

coordinate scatter method is a qualitative method, and the comprehensive method is an integration of these two methods. In this study, we adopted the mean value method. The mean value of the market share and growth rate of all tourism markets during the time t is used to determine the value of a and b for period t. The formulae are as follows:

at ¼

n X

!,

ati

i¼1

t

b ¼

n X i¼1

n

(1)

!,

bti

n

(2)

wherein ati is the market share of the i-th source country in t-th period;

bti is the market growth rate of the i-th source country in t-th

Fig. 1. Four types of market forms in a tourism system.

period; and n refers to the total number of source countries, and n ¼ 9.

corporate product mix methodologies. The method reveals market structure and development path (Li, Yang, & Zhao, 2012). The formulae for the market form of tourism systems and its shift-share model are as follows:

Xt Market share : ati ¼ Pn i

i¼1

t

Xit

Market growth rate : bi ¼

The shift in market form reveals the dynamic changes of a given market structure within the time series (Yao & Sun, 2008). When not influenced by the changes in external factors, the market form of a given market generally follows the shifting process of “Dog /Child/Star /Cash Cow” (Huang & Wu, 2015). When influenced by external environmental conditions, the development of market forms tends to take one of the five dynamic shifting forms: remaining stable, incremental growth, leaped growth, cyclical fluctuation, and steady decline (Wu, Zhu, & Sun, 2015), as is shown in Fig. 2.

 100%

Xit  Xit1 Xit1

 100%

where ati is the market share of the i-th source country in the t-th period;

2.2.2. Tourism flow distance decay pattern The distance decay law is a basic geography principle, which has been widely employed in tourism flow research. The tourism flow distance decay indicates that the number of visitors entering a certain destination decreases as the travel distance increases (Gregory et al., 1988). In accordance with this phenomenon, some scholars distinguish travel flows into different distance decay curve types, which are drawn based on actual market survey data. Such curve types are general, U-shape, and Boltzman pattern (Taylor, 1971; Wu, Tang, & Huang, 1994). As shown in Fig. 3, in the general pattern, the distance factor in tourists' travel choice is resistance, that is, the number of visitors decreases as the destination distance gradually increases. On the contrary, in the U-shape pattern, the distance factor is turned into resistance depending on the distance: when the distance to destination is within a certain range the distance factor is resistance, resembling the general pattern; however, over such range the distance might become thrust, which happens when people's curiosity rises as the distance increases. The Boltzman pattern is the opposite of the U-shape pattern: at first, visitors show more curiosity and desire for travel when the distance increases, which leads to a rising curve; however, above a certain distance, the curve

bti is the market growth rate of the i-th source country in the t-th period; Xit is the number of visits of the i-th source country in the t-th period; and n refers to the total number of source countries. In this study, n ¼ 9. The two-dimensional area formed by market share (ati ) and growth rate (bti ) of a given source country represented its market form Uti ðati ; bti Þ in time t. We established a reasonable classification criteria Ut ða; bÞ, and based on the market forms of the source countries, we divided the tourism markets of the countries into four types: Star market, cash cow market, child market, and dog market (Li & Sun, 2002), as is shown in Fig. 1. The basic features of the four market forms are listed in Table 1 (WEF., 2015; Wei & Gan, 2010). For the classification criteria Ut ða; bÞ, there are three methods to define a and b: the mean value method, the coordinate scatter method, and the comprehensive method (Zhou & Zhao, 2004). Here, the mean value method is a quantitative method, the

Table 1 Basic features of the four market forms. Market Forms

Standards

Market Character

Dog Market Child Market Star Market Cash Cow Market

a
Market Market Market Market

share share share share

and growth rate are both low, and the market is in recession. is low, but the market growth rate is high, and the market is recovering or growing. and growth rate are both high, and the market is expanding. is high, but the market growth rate is low, and the market is in a mature stage.

Y. Liu et al. / Tourism Management 64 (2018) 22e36

25

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi , u n u1 X  2 CV ¼ t Xi  X X n

(4)

i¼1

where n is the number of samples, Xi is the value of the i-th sample, and X is the mean value of the samples. ③The Gini coefficient (G) is a measure of the degree of equality within a region, with a value between 0 and 1. If Gini coefficient is 0, the development in the region shows perfect equilibrium. If Gini coefficient is 1, the development in the region shows non-perfect equilibrium. The smaller the value, the more balanced the development within a region. Its calculation is as follows:

G¼1

Fig. 2. Market Form and its Shift-share Model.

exhibits a declining trend, because of increasing visitors' finance and time constraints.

2.2.3. Indicators of market scale differentiation Scale differentiation can be measured with standard deviation (SD), coefficient of variation (CV), Gini coefficient (G), primacy index (S), and the Herfindahl-Hirschman index (HHI). ①Standard deviation (SD) is the square root of the average of the squared deviations of the values from their average value, which is a kind of statistical indicator presenting dispersion degree and can be used to measure the absolute differences among the market scale of source countries (Tang, Song, & Li, 2014). The formula can be written as follows:

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u n u1 X  2 SD ¼ t X X n i¼1 i

(3)

where n is the number of samples, Xi is the value of the i-th sample, and X is the mean value of the samples. ②Coefficient of variation (CV), also known as relative standard deviation (RSD), refers to the scale and degree of each sample from the center of the distribution and is an indicator of the measure of dispersion. The formula is as follows:

n1 X 1 2 Wi þ 1 n i¼1

! (5)

where n is the number of samples, and Wi is the proportion of the number between the first tourist to the i-th tourist to the total number of tourists. ④The primacy index is an indicator used to measure the degree of concentration of a given market distribution. The core idea is to study the degree of importance of the largest market, and the ratio between the largest market and the second largest market, in terms of scale, which is defined as S (Shen, Han, & Li, 2006). Usually, an S less than 2 is considered a normally structured market with an appropriate degree of concentration, while an S greater than 2 suggests an imbalanced market structure due to a tendency of excessive concentration. Its formula can be described as

S ¼ P1=P ; 2

(6)

where P1 is the size of the largest market and P2 is the size of the second largest market. ⑤The Herfindahl - Hirschman index, or HHI, is a comprehensive indicator used to measure market concentration. It is the sum of squares of the market share of each player in a given market, and thereby, calculates the variation in their market share, or the degree of dispersion of market players. The closer HHI is to 1, the more concentrated the market is. The calculation is as follows:

HHI ¼

n X

ðXi =XÞ2

(7)

i¼1

where n is the number of samples, Xi is the size of the i-th sample, and X is the total size of all samples.

Fig. 3. Tourism flow distance decay pattern.

26

Y. Liu et al. / Tourism Management 64 (2018) 22e36

Among the five indicators, SD, CV, and G mainly reflect the balance of the market share. A greater value indicates less balance among players in a given market, and greater the differentiation of their market share is. S and HHI are used to analyze the degree of concentration in a market. The greater the value, the more concentrated the market in the development of inbound tourism, and the less balanced the development of the market. 3. Data illustration 3.1. Changes in the number of inbound visits by ASEAN tourists Besides Thailand, ASEAN member countries include Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, the Philippines, Singapore, and Vietnam. The number of inbound tourists from each ASEAN country during 1996e2015 is shown in Table 2 and Fig. 4. As shown in Table 2 and Fig. 4, there had been a continuous

growth in the number of Thailand's inbound tourists from ASEAN countries between 1996 and 2015. Malaysia was the largest tourism source country, with a proportion of about 50% of total inbound tourists. In 2015, visits by Malaysian tourists reached 3,423,400, an increase of 2.27 times over 1996, when the count was 1,046,200. From 1996 to 2015, tourist visits from Singapore had increased by 1.6 times; however, there seems to be an apparent fluctuation after 2007, with the highest value exceeding 1 million in 2013. Other countries, such as Laos, Vietnam, and the Philippines, had fewer tourists entering Thailand before 2005, far behind that of Malaysia and Singapore. Nonetheless, since 2006, tourists from these countries have increased significantly, with Vietnam rising to fourth place. With the opening of the Thai-Lao Friendship Bridge, transportation between Thailand and Laos has become more convenient. As a result, Laos has surpassed Singapore and become the second largest ASEAN source country for Thailand in recent years. Among ASEAN countries, the Philippines is located the

Table 2 Inbound Visits from each ASEAN Country (1996e2015; in 10,000s). Year

Malaysia

Singapore

Laos

Vietnam

Indonesia

Philippines

Cambodia

Myanmar

Brunei

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

104.62 102.91 103.16 100.98 105.45 115.96 129.61 133.86 138.90 134.15 157.86 155.20 182.83 174.83 204.72 249.20 254.61 303.11 261.34 342.34

36.07 40.59 49.72 52.89 65.58 66.50 68.33 62.91 73.22 79.53 81.82 79.91 65.20 65.15 65.43 78.93 99.46 106.73 84.41 93.73

2.25 2.65 4.73 6.88 7.48 8.64 9.41 10.45 11.64 20.81 28.22 52.11 62.46 65.77 71.84 89.54 98.11 98.49 105.40 123.31

1.86 2.15 3.95 3.43 5.70 8.01 9.80 13.51 15.58 19.55 25.18 25.43 35.57 38.18 39.74 51.48 63.73 74.07 55.94 75.11

8.36 9.00 6.95 13.32 14.51 15.35 16.50 16.74 20.13 18.67 21.82 23.39 25.93 22.65 28.57 37.07 44.94 59.50 49.76 46.92

7.77 7.89 8.25 8.68 10.67 12.98 14.29 14.30 17.32 18.84 20.23 19.89 22.40 21.52 24.29 26.28 28.06 31.50 30.48 31.10

1.53 1.50 2.35 4.88 4.31 5.44 7.92 7.39 9.86 11.25 12.53 10.88 9.33 10.32 15.00 27.13 43.05 48.70 55.03 48.75

5.36 4.37 5.65 4.15 4.72 4.29 4.23 3.72 4.60 5.65 6.71 7.52 7.57 8.01 9.11 11.15 12.97 17.33 20.68 25.97

0.39 0.43 0.52 0.80 1.28 1.39 1.38 1.72 1.39 1.51 1.27 1.24 1.24 1.05 0.89 1.01 1.33 1.62 1.13 1.38

Data Source: Statistical Yearbook Thailand, Statistical Yearbook of Tourism Authority of Thailand, and Statistics Yearbook of the Thai Immigration Office.

Fig. 4. Trend of inbound ASEAN tourists (1996e2015).

Y. Liu et al. / Tourism Management 64 (2018) 22e36

farthest from Thailand. Although its number of tourists to Thailand increased more than three times in 2015 compared to 2009, the absolute number was still relatively small, accounting for only 4% of all ASEAN tourists. Since 2010, the number of tourists from Cambodia and Indonesia has increased substantially. By 2015, the two countries had risen to the 5th and 6th place, respectively. Brunei is the least populated country within the ASEAN region, with a population of about 420 thousand. Although it has a relatively higher level of consumption and economic development, owing to its relatively distant geographical location and small population base, it has the least amount of inbound visits to Thailand coupled with an insufficient growth potential.

27

3.2. Total inbound visits and changes in foreign exchange earnings After compiling data collected from the Statistical Yearbook Thailand, the Statistical Yearbook of Tourism Authority of Thailand, and the Statistics Yearbook of the Thai Immigration Office, the figure for Thailand's inbound tourists and its foreign exchange earnings during 1996e2015 are presented in Table 3, Fig. 5, and Fig. 6. Based on Table 3 and Fig. 5, from 1996 to 2015, inbound ASEAN tourists maintained incremental growth, with only two exceptions in 2009 and 2014, when the number dropped. This could be because, in 2009, the north and central parts of Thailand and

Table 3 Inbound visits and Thailand's foreign exchange earnings (1996e2015). Year

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Inbound Visits

Foreign Exchange Earnings

Number (ten thousand)

Growth Rate (%)

Number (million dollar)

Growth Rate (%)

168.19 171.48 185.28 196.01 219.68 238.55 261.46 264.60 292.63 309.96 355.64 375.56 412.52 407.47 459.59 571.80 646.26 741.04 664.18 788.61

e 1.95 8.04 5.79 12.08 8.59 9.60 1.20 10.59 5.92 14.74 5.60 9.84 1.22 12.79 24.42 13.02 14.67 10.37 18.74

988.90 966.77 954.00 957.44 1029.86 1024.31 1036.02 1447.53 1670.84 1147.49 1893.00 2336.73 2643.19 2409.57 3077.85 4293.50 5259.70 6373.48 5646.76 7779.54a

e 2.24 1.32 0.36 7.56 0.54 1.14 39.72 15.43 31.32 64.97 23.44 13.11 8.84 27.73 39.50 22.50 21.18 11.40 37.77

a This is an estimated value rather than the actual statistics, because only the statistics of the first half of 2015 were obtained via the information published by the Tourism Authority of Thailand. Given that the foreign exchange earnings from ASEAN tourists were USD 3301.58 million for the first half of 2015, an increase of 37.77% over the same period in 2014, we estimated the total earnings in 2015 to be USD 7779.54 million.

Fig. 5. Trends in Thailand's total inbound visits (1996e2015).

28

Y. Liu et al. / Tourism Management 64 (2018) 22e36

Fig. 6. Trend of changes in Thailand's foreign exchange earnings generated by inbound tourism from ASEAN countries (1996e2015).

Bangkok were heavily flooded, and domestic political tensions and rioting at the end of the year caused a significant negative impact on inbound tourism. Furthermore, the 2014 military coup resulted in continuous instability and led to a decline in inbound visits. Based on the growth pace of tourist numbers, we divided the 20year period into two phases: Phase I (1996e2009) was defined as a steady growth period, with an average increase of 7.13% in the number of tourists from ASEAN countries. Despite a tsunami at the end of 2004, the upward trend in visits was not interrupted. Phase II (2010e2015) was considered a period of leaped growth. During 2010e2013, the average annual growth in the number of tourists reached 17.37%. After negative growth in 2014, it bounced back to 18.74%. The average annual growth rate of the five years was 12.21%. Apparently, as long as the domestic situation of Thailand is stable, and with the establishment of the AEC, inbound tourism between Thailand and other ASEAN countries should achieve more rapid development. Fig. 6 shows a consistent increase in Thailand's foreign exchange earnings generated by inbound tourism from ASEAN countries between 1996 and 2015, with the exception of three obvious drops in 2005, 2009, and 2014. Although the tsunami at the end of 2004 did not affect the trend of growth in visits from ASEAN countries, Thailand's foreign exchange earnings fell by 31% in 2005. Riots in 2009 and a military coup in 2014 severely affected the country's tourism industry, leading to a reduction in its foreign exchange earnings and inbound visits. Based on the growth rate of foreign exchange earnings, the 20-year period was divided into two corresponding phases of growth: In phase I (1996e2009), inbound tourists increased, although the increase curve of foreign exchange earnings was relatively flat, with an average annual growth of 9.39% and two significant drops during the period. There was one substantial drop in phase II (2010e2015); however, the average annual growth of foreign exchange earnings still reached 22.88%. This was the fastest period of growth in Thailand's foreign exchange earnings, corresponding to the rapid growth in inbound visits. The growth of foreign exchange earnings was also significantly higher than the average growth of the number of visits. Therefore, this period was the golden age of Thailand's inbound tourism development. The rapid growth could be attributed to the implementation of visa-free policies among ASEAN members; the

opening up of Myanmar, Cambodia, and Laos; and growth in the national economic income of ASEAN countries, which stimulated the demand for outbound tourism and promoted consumption among tourists. 4. Results 4.1. Market forms and dynamic shifting tracks of source countries from 1996 to 2015 4.1.1. Market forms of source countries Using the number of inbound visits by ASEAN tourists, as the original data, and by applying the mean value method, we calculated and analyzed market forms and the dynamic shifting of market forms of ASEAN countries during 1996e2015: 4.1.1.1. Market form distribution of source countries of Thailand's inbound tourism during 1996e2015. First, based on data in Table 1, we calculated the market form classification criteria of ASEAN countries U19962015 ða; bÞ using the mean value method, and obtained a ¼ 11.11% and b ¼ 12.96%. Next, we calculated the scatter plot of these countries’ markets form during 1996e2015 (seen in Fig. 7), based on Table 1, Formula (1) and (2). According to market share data, rate of growth, and the scatter plot shown in Fig. 7, the source countries were grouped into four types, as exhibited in Table 4. Fig. 7 and Table 4 show that the four types of market forms in each source country belonged to: Dog Markets e Indonesia, the Philippines, Brunei, and Myanmar. In 20 years, no significant difference was found in the growth of inbound tourism market of these four countries. All of them had an average market growth rate greater than 8%, and their market share was far from average (a ¼ 11.11%). Brunei had the lowest market share of 0.34%, far behind other ASEAN countries. Child Markets e Cambodia, Vietnam, and Laos: Although these countries had a lower market share, their market growth rate exceeded 20%, much higher than average (b ¼ 12.96%), indicating a higher growth potential. Cash Cow Markets e Malaysia and Singapore: These two countries seem to be the steady sources of Thailand's inbound tourism. They had the highest market share, with that of Malaysia reaching 47.05%, far

Y. Liu et al. / Tourism Management 64 (2018) 22e36

29

Fig. 7. Scatter plot of market forms of source countries (1996e2015).

Table 4 Market forms of source countries (1996e2015). Market Form

Standards

Source Countries

Market Form (%)

Dog Market

a<11.11% b<12.96%

Indonesia Philippines Brunei Myanmar

(6.23, (5.00, (0.34, (2.18,

Child Market

a<11.11% b  12.96%

Cambodia Vietnam Laos

(3.48, 23.10) (6.00, 23.25) (8.93, 20.65)

Star Market

a  11.11% b  12.96%

None

Cash Cow Market

a  11.11% b<12.96%

Malaysia Singapore

above the line of classification criteria. Star Markets e vacant: No country had both the market share and growth rate higher than other countries. 4.1.1.2. Changes in the distribution of market forms of the source countries during 1996e2005 and 2006e2015. In addition, we compared the market forms of the source countries during 1996e2005 and 2006e2015 to reveal the dynamic changes in them. The mean value method was employed and the classification criteria of the two phases, U19962005 (11.11%, 14.33%) and U20062015 (11.11%, 11.59%), were obtained. Next, we computed the market form according to formulas (1) and (2). The results are

10.98) 8.07) 9.27) 9.07)

(47.05, 6.40) (20.79, 5.86)

shown in Table 5 and Fig. 8. As shown in Table 5 and Fig. 8, the market forms of source countries changed during 1996e2005 and 2006e2015. Myanmar developed from a dog market into a child market, Laos developed from a child market into a star market, whilst Brunei degraded from a child market to a dog market. Moreover, the number of countries that belonged to the child market category reduced from four to three within the ASEAN countries. The star market was no longer vacant, and Malaysia was very close to becoming a star market. In addition to the overall changes in the types of market forms, the market shares and growth rates of each source country experienced different degrees of changes. The market share of Malaysia dropped

Table 5 Market form distribution of source countries during 1996e2005 and 2006e2015. Market Form

1996e2005

2006e2015

Standards

Source Countries

Market Form (%)

Standards

Source Countries

Market Form (%)

Dog Market

a<11.11% b<14.33%

Indonesia Philippines Myanmar

(5.93, 10.98) (5.13, 10.81) (2.13, 1.30)

a<11.11% b<11.59%

Indonesia Philippines Brunei

(6.53, 10.97) (4.87, 5.33) (0.24, 1.02)

Child Market

a<11.11% b  14.33%

Vietnam Cambodia Laos Brunei

(3.27, (2.26, (3.42, (0.45,

30.45) 26.78) 19.97) 17.52)

a<11.11% b  11.59%

Vietnam Cambodia Myanmar

(8.73, 16.05) (4.69, 19.42) (2.24, 16.83)

Cash Cow Market

a  11.11% b<14.33%

Malaysia Singapore

(51.69, 2.13) (25.72, 9.06)

a  11.11% b<11.59%

Malaysia Singapore

(42.41, 10.67) (15.85, 2.66)

Star Market

a  11.11% b  14.33%

None

a  11.11% b  11.59%

Laos

(14.44,21.33)

30

Y. Liu et al. / Tourism Management 64 (2018) 22e36

Fig. 8. Scatter plot of market forms of source countries during 1996e2005 and 2006e2015.

from an average of 51.69% during 1996e2005 to an average of 42.41% during 2006e2015. Singapore's market share also dropped by nearly 10%, whereas Laos maintained a growth rate of nearly 20% and achieved an 11% growth in market share, exceeding the average level, and entering the star market category. Among the source countries, Brunei's decline was most apparent; its market growth dropped from an average of 17.52% during 1996e2005 to an average of 1.02% during 2006e2015, while the market share dropped from 0.45% to 0.24%. The Philippines and Singapore also experienced a reduction in both indicators; however, Singapore retained its position in the cash cow market category. 4.1.2. The dynamic shifting tracks of the market form of source countries In addition to the static analysis of the market forms of source countries, we conducted analyses to track the dynamic shifting process of their market forms during 1996e2015. Based on the classification criteria U19962015 (11.11%, 12.96%), the shifting tracks of the market forms of source countries can be found in Fig. 9. As can be seen in Fig. 9, we found the following shifting patterns: After 20 years of development, Laos achieved large-scale evolution from a dog market to a star market. This could be attributed to the opening of the Thai-Lao Friendship Bridge in 1994, which led to rapid growth in the number of inbound tourists. In 1998, 2005, and 2007, the growth rate of inbound tourists from Laos reached almost 80%. During 2006e2007, Laos accomplished transformed from a child market to a star market, and its market share increased to approximately 15%. Since 2007, Laos’ market share and growth rate started to stabilize and its market growth rate remained close to the benchmark threshold. Between 1996 and 2015, Singapore retained a high market share of Thailand's inbound tourism; however, the market growth rate fluctuated greatly, and the country experienced seven changes between cash cow market and star market. Given that Singapore is an important source country for Thailand's inbound tourism market, the changes in its market forms are closely related to changes in the political status in Thailand and other Southeast Asian countries. The Asian financial crisis between 1998 and 2000 caused a substantial decline in visits by Singaporean tourists to Thailand during 2000e2001 and 1998e1999, and Singapore degraded from a star market to a cash cow market. The riots in Thailand in 2006 resulted in a 15% negative growth in visits from Singaporean

tourists. In 2011 and 2013, the multiple bombing incidents in Bangkok resulted in Thailand's inbound tourism from Singapore reaching its lowest growth rate in history. This finding indicates that Singaporeans pay more attention to safety in their travels. If there were major incidents, safety would be their first priority. Malaysia had been the largest source country for Thailand's inbound tourism, with an average market share of approximately 50%. The scatter plot in Fig. 9 revealed a year-on-year reduction in the market share of Malaysia between 1996 and 2015. Until 2000, its market growth rate had remained lower than 5%. In addition, influenced by the political situation in Thailand and other Southeast Asian countries, its market growth rate experienced large fluctuations. With better cooperation among ASEAN countries and domestic stability in Thailand, in 2015, Malaysia progressed from being a cash cow market to a star market, with a market growth rate of greater than 30%, far exceeding the average market growth rate of the previous 20 years (11.56%). Apparent fluctuations were noted in Vietnam and Cambodia's market form matrix; however, their number of tourists to Thailand maintained steady growth. A clear downward trend was observed in the growth rate of the Vietnamese market over 20 years; however, Vietnam's market share increased from less than 1% in 1996 to nearly 10% in 2015. In addition, Vietnam was positioned in the child market category for the majority of the period. Cambodia's market growth reached its highest in 1999 and retained a steady growth between 2000 and 2010. No major differences were detected in its market share over 20 years; however, there was rapid growth after 2010, and its market share increased to more than 8%. Although the market growth rate of the Philippines, Myanmar, Brunei, and Indonesia experienced great changes over the years, the largest variations appeared when Thailand was experiencing domestic political instability or natural disasters. Its market share was relatively stable, mostly between 0.4% and 8%, without exceeding 8%. Brunei had the lowest market share among the ASEAN countries, without any noted record that exceeded 1%. In summary, large variances were found among the market forms of source countries. Malaysia's market was at the forefront of all countries. However, with rapid economic development of Laos and Cambodia in the last 10 years, outbound tourism has also shown rapid increase. In addition, since these countries are geographical neighbors to Thailand, they are source countries with high potential for development.

Y. Liu et al. / Tourism Management 64 (2018) 22e36

31

Fig. 9. Shifting tracks of market forms of source countries (1996e2015).

4.2. The impact factors of Thailand inbound tourists’ flows from ASEAN countries 4.2.1. Establishing a two-dimensional structure curve Market structure and its variation law of Thailand inbound tourists from ASEAN countries over twenty years are analyzed through the rates of market occupation and growth without accounting for the influence of other factors. Actually, there is a close connection between inbound market structure and tourism stream from source countries, as tourism flows are usually affected by factors such as distance, economy, source country population, bilateral trade, etc. In order to analyze whether these factors affect Thailand's inbound market structure from ASEAN countries, we adopt the distance decay pattern and the two-dimensional scatter graph. As a consequence, we can also explain whether other factors impact the number of inbound tourists from ASEAN countries through a visual and simple method, and reveal the market characteristics of these source countries. Specifically, in the existing literature, the distance decay pattern is employed to draw the two-dimensional scatter graphs based on distance and tourist quantity. Starting from this, we claim that the method can also be used to concisely and intuitively show whether economic income, population size, and bilateral trade variables impact tourist flows, provided that the law for which tourism flows are proportional to these three factors holds. Therefore, based on the relevant 2015 data (Table 6), we resort to the distance decay pattern to draw two-dimensional scatter graphs taking “distance”, “GDP per capita”, “population size,” and “bilateral trade” on the

horizontal axis, and the “number of tourists” on the vertical axis. The results are shown in Fig. 10.

4.2.2. Interpretation of the two-dimensional structure curves Fig. 10 can be interpreted as follows. (1) In Fig. 10-a considering travel distance, the tourist flow curve from 9 ASEAN countries in 2015 exhibits basically the same pattern as that of the Boltzman curve in Fig. 3. The distance factor mainly acts as a thrust within 1191 km, that is, driven by curiosity and other factors, the tourists from 9 ASEAN countries have more desire to travel as the distance increases. As a result, the number of inbound tourists from the considered countries is on the rise. However, beyond 1191 km, the distance factor becomes a resistance, that is, if the distance further increases, factors such as time and economy become constraints, resulting in a gradual decline of visitors. (2) In panels b and c of Fig. 10, the economic and demographic factors do not show significant impetus or resistance, i.e., there are no obvious patterns or characteristics in the number of inbound tourists with the variation of GDP per capita population size in the 9 ASEAN countries. This might imply that economic and demographic differences among ASEAN countries are not the key factors contributing to the variations in Thailand inbound tourists’ flows. (3) The curve in Fig. 10-d shows that, although Indonesia and Laos exhibit fluctuating peaks due to other factors (Laos is bordering on Thailand, while Indonesia is the furthest from Thailand), with the continuous growth of bilateral trade between

32

Y. Liu et al. / Tourism Management 64 (2018) 22e36

Table 6 Statistics from ASEAN countries.a Source Countries

Laos Cambodia Myanmar Vietnam Malaysia Singapore Brunei Philippines Indonesia a b

2015 Tourist Scale/ten thousand

Distance from Thailand/kmb

GDP Per Capita/dollar

Population/ten thousand

Bilateral Trade/million dollar

123.31 48.75 25.97 75.11 342.34 93.73 1.38 31.10 46.92

530 540 818 980 1191 1425 1857 2215 2319

1818 1159 1161 2111 9768 52,889 30,555 2904 3347

691 1560 5390 9513 3033 554 42 10,070 25,800

5871.22 5595.54 6529.55 13 852.71 20 526.52 14,725.81 702.37 9106.93 14,452.82

Statistics on distance, GDP per capita, population size, and bilateral trade of Thailand's inbound tourists from ASEAN countries. The distance from Thailand is the direct distance between Bangkok and the capital of each ASEAN country.

Fig. 10. Relational Graphs between the Number of Tourists and Distance (a), GDP per capita (b), Population Size (c), and Bilateral Trade (d).

Thailand and ASEAN countries, inbound tourists present an overall growing trend. This shows that the more intense the bilateral trade an ASEAN country has with Thailand, the higher the number of tourists entering Thailand. Therefore, the development of bilateral trade between Thailand and other ASEAN countries has a positive effect because of the increase of business visitors. Thus, bilateral trade emerges as a very important factor incentivizing ASEAN tourists to travel to Thailand. (4) In contrast to the above-mentioned analysis on market structure, we find that Indonesia, the Philippines, and Brunei have been lingering on Dog or Child markets in the past 20 years. The distance from the three source countries to Thailand is far more than 1191 km, and Brunei has the smallest population. Therefore, it is difficult for the three countries to become Cash Cow or Star markets in the coming years since the development potential is limited. As the second source of Thailand's inbound tourists from 9 ASEAN countries, Laos has developed into a Star market over the

past 10 years. Although it is not superior in terms of GDP per capita, bilateral trade, and population size, it borders Thailand and enjoys a neighborhood national relationship, which becomes a driving power for Laos tourists to travel to Thailand. Meanwhile, Singapore and Malaysia are the Cash Cow markets. Singapore has the highest GDP per capita among the ASEAN countries, and it is ranked second in bilateral trade after Malaysia, which also has a high GDP per capita. Therefore, there is a greater potential for these two countries to become Star markets in the future. 4.3. Scale differentiation of source countries By collecting statistical data of inbound tourists (Table 1) to Thailand from ASEAN countries during 1996e2015 issued by the Tourism Authority of Thailand, the Immigration Bureau of Thailand, and the Ministry of Commerce of Thailand, we calculated the SD, CV, G, S, and HHI of the source countries according to formulae

Y. Liu et al. / Tourism Management 64 (2018) 22e36 Table 7 Indicators of scale differentiation of source countries (1996e2015). Year

SD

CV

G

S

HHI

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

340,443 338,142 344,440 336,885 362,495 389,535 427,429 432,224 451,978 437,707 501,013 490,926 561,883 539,603 624,267 754,963 765,088 898,905 770,645 1,024,389

1.8217 1.7747 1.6731 1.5468 1.4851 1.4696 1.4713 1.4701 1.3901 1.2709 1.2679 1.1765 1.2259 1.1919 1.2225 1.1883 1.0655 1.0917 1.0443 1.1691

0.7122 0.7077 0.6758 0.6476 0.6322 0.6238 0.6181 0.6119 0.5939 0.5536 0.5549 0.5449 0.5487 0.5425 0.5414 0.5275 0.4928 0.4898 0.4714 0.5098

2.9007 2.5353 2.0746 1.9093 1.6079 1.7438 1.8968 2.1278 1.8970 1.6867 1.9294 1.9421 2.8042 2.6584 2.8497 2.7832 2.5598 2.8399 2.4796 2.7762

0.4389 0.4222 0.3876 0.3474 0.3328 0.3244 0.3249 0.3246 0.3020 0.2706 0.2699 0.2478 0.2595 0.2514 0.2587 0.2506 0.2232 0.2288 0.2188 0.2461

from (3) to (7). The results are shown in Table 7, and the changes in each indicator are exhibited in Figs. 11 and 12. Table 7, Fig. 11, and Fig. 12 reveal that. (1) The overall development of Thailand's inbound tourism seems balanced. Although the SD value dropped during 1996e1997 and 2013e2014, due to domestic instability and the poor economic status of Thailand, the SD continuously increased over 20 years. Compared to 1996, the SD of 2015 increased by 2 times, an average annual growth rate of more than 15%, suggesting a constant increase in the absolute value of scale differentiation of source countries. The overall CV of source countries manifested a downward trend, from 1.82 in 1996 to 1.17 in 2015, a decrease of 35.82%. Nonetheless, the value remained greater than 100%, indicating a relatively high degree of dispersion within the market. The number of tourists from the nine ASEAN countries was not evenly distributed. Simultaneously, G also showed a

33

downward trend, falling 28.41% over 20 years, suggesting a tendency toward balanced development. (2) The distribution of Thailand's inbound tourism market showed a significant primacy distribution characteristic. Usually, an S less than 2 is considered a normally structured market, with a medium degree of concentration, whilst an S value greater than 2 suggests an imbalanced market structure, and a tendency toward excessive concentration. According to Table 7 and Fig. 12, the S was greater than 2 in some years. Malaysia was the largest market among the source countries during 1996e2015, far ahead of other countries in number of tourists. Singapore ranked second. Although there was rapid growth in the number of inbound visits by tourists from other countries, the absolute values were still relatively low. The primacy index curve in Fig. 12 fluctuated around the value 2 over 20 years, and has remained greater than 2 since 2008. This finding indicates that the market structure of the source countries remained unbalanced in recent years, and no significant improvement was noted in the excessive concentration of the market. (3) The differentiation in market scale has gradually reduced, but the degree of concentration of the market has remained high. The HHI is an indicator that measures the degree of concentration of the tourism market in a given region. The closer the HHI is to 1, the higher the degree of market concentration within a region. As shown in Table 7 and Fig. 12, the HHI dropped from 0.44 to 0.24 during 1996e2015, a decrease of 43.92%, suggesting that the scale differentiation is reducing; the value, however, was still greater than 0.2. To sum up, the degree of balanced development among the source countries awaits further improvement. 5. Conclusions and discussion 5.1. Conclusions The analyses of the number of inbound tourists from the source countries and Thailand's foreign exchange earnings from 1996 to 2015 showed that the development of Thailand's inbound tourism

Fig. 11. Changes in SD of market scale of source countries (1996e2015).

34

Y. Liu et al. / Tourism Management 64 (2018) 22e36

Fig. 12. Changes in CV, G, s, and HHI of market scale of source countries (1996e2015).

had maintained sustained growth, with slight, occasional “declines.” Malaysia appears to be the largest source country for Thailand's inbound tourism among ASEAN countries. In addition, the growth rate of foreign exchange earnings seems to be higher than the growth rate of the number of visits. An investigation of the market forms of source countries reveal that the source countries are mainly distributed in the dog and child market categories. However, regardless of whether the equations are computed based on a 20-year period or two 10-year periods, Laos developed into a star market in 2015. Malaysia is positioned in the star market category when calculated based on a 20-year period. If based on two sets of 10-year periods, it is positioned close to the star market category for 2006e2015. The dynamic shifting analysis shows significant differences in the shifting tracks of the source countries. Malaysia, Singapore, Vietnam, and Cambodia have a greater potential in market development than other ASEAN countries. Based on the analysis of the influencing factors of Thailand inbound tourists’ flows from 9 ASEAN countries in 2015, we find that there is a significant Boltzman curve characteristic between the number of tourists and the travel distance. Moreover, the bilateral trade between Thailand and other ASEAN countries emerge as a significant factor influencing the increase in the flow of inbound tourists, while the impact of GDP per capita and population size do not seem to be significant. The results of the calculation of scale differentiation suggest that Thailand's inbound tourism market is characterized by primacy distribution, with Malaysia being the largest source country. There seems to be a decline in the degree of scale differentiation; however, the extent of market concentration remains high. On that account, the realization of balanced development in the market of source countries can provide growth potential for Thailand's inbound tourism market, and should be the direction of its market structure improvement endeavors. 5.2. Theoretical contributions and practical implications First, the theoretical contributions of this study are based on BCG to suggest a tourism market form that is constructed from time series data pertaining to inbound tourists from ASEAN source

countries entering Thailand over a 20-year period. The study scientifically presents the market trend and market structure of Thailand's inbound tourism with the help of BCG matrix theory. It also plots the dynamic shifting track showing the relationship between growth in tourist numbers from nine other ASEAN countries and their market share in Thailand's inbound tourism by respectively calculating their annul market structure of every country. That is to say, the BCG theory should be improved in our paper to some extent. Thus these results reflects trend changes in the tourism market, and clarifies the distribution of ASEAN tourists visiting Thailand and the potential market. This is also the first study to employ standard deviation, coefficient of variation, the Gini coefficient, primacy index, and HHI indicators to measure the scale differentiation of the inbound tourist market, revealing significant differences among the nine source countries. The status of the segment market structure is not only analyzed, and also the integrated features of the market structure has been examined. Moreover, when some researchers applied BCG to analyzed tourism market structure, they named it a tourism market competitive form. We think this name is not appropriate. Hence, we suggest its name should be a tourism market form. At the same time, we have also modified the names of four quadrants. That is, we have translate thin-dog market, child market, bright-star market, and golden-ox market in some Chinese papers into dog market, child market, star market, and cash cow market avoiding Chinese English. Second, the practical applications of the research findings could serve as a reference to help Thai tourism management departments in the formulation of policies to enhance the competitiveness of Thailand inbound tourism. In particular, it provides a clear focus for the development of measures to attract more inbound tourists from ASEAN source countries. Furthermore, the research also reflects the potential pressure faced by the Thailand tourism market. For example, although Malaysia is its largest visitor source with a market share of about 50%, this figure is declining, causing it to lose its “star market” status, and we could also find that business tourists might account for a large share in the two-dimensional curve between number of tourists and bilateral trade. The analysis provided in this study (see Fig. 7) will offer insights in this aspect for Thailand tourism management departments and

Y. Liu et al. / Tourism Management 64 (2018) 22e36

enterprises. First, Laos, Vietnam and Cambodia are the fastest growing tourism markets (child market), and Thailand's closest neighbors, with relatively convenient transportation and similar religion–Theravada Buddhism. Therefore, we recommend that Thailand should at least improve road connectivity with these countries to enhance its tourism appeal, thereby converting these countries into cash cow markets or even star markets (Laos is close to becoming a star market). Second, Indonesia (world's fourth most populous country) and the Philippines are both populous countries with strong development potential. Although these are still dog markets and their economy and population size have not been relevant factors over the past 20 years, they should be Thailand's targeted source countries in the future, with their increasing population size. Tourists from these countries mainly consist of Muslims and other religions. Therefore, the Thai tourism departments should ensure that there are tourist attractions and facilities in major cities that are suited for Muslim believers to attract more tourists from these populous countries. This will further enhance the competitiveness of Thai tourism amongst ASEAN countries. Third, it is still necessary to stabilize the cash cow markets, such as Singapore and Malaysia, through innovative tourism products, creating new brands and increasing marketing, as well as providing a more convenient trade environment to attract business tourists, to enhance the rate of returning visitors. Finally, our findings reveal the changes and trends of inbound tourist numbers and foreign exchange earnings in the past 20 years. It clearly reflects the sharp simultaneous decrease in tourist numbers and foreign exchange earnings in 2009 and 2014. This can be traced back to the unrests that occurred in 2008 and 2013. Hence, the impact of unrest on inbound tourist numbers and foreign exchange earnings is only reflected in the following years. Therefore, this serves as a reminder for the Thai Government to ensure stability in the nation to enhance the competitiveness of its pillar industry. 5.3. Limitations and suggestions for future research This study collected a large amount of statistical data and conducted in-depth analysis on the scale differentiation, flow trends, and shifting tracks of inbound tourists from ASEAN source countries, and has obtained valuable results and conclusions. Nevertheless, it still has its limitations. The time series and scale differentiation of inbound visitor flow from ASEAN source countries were analyzed based solely on tourist number. We have yet to collect additional data to carry out analysis on spending, length of stay, and preference of tourists from individual ASEAN source countries. In addition, we have not conducted comparative analysis of the inbound tourism competitiveness of respective source countries to investigate potential problems existing in Thailand's inbound tourism market. This will provide a better picture of Thailand's market position and competitiveness in inbound tourism. In addition, future studies should perform a comprehensive comparative analysis on Thailand's overall inbound tourism market. For example, comparative analysis can be performed for the distribution pattern and variation of Asian source countries, such as China and Japan. Such studies will offer better suggestions for improvement to the Thai tourism department and relevant enterprises. Therefore, all these research topics warrant further exploration in the future. Notes In the BCG Model, the four markets are divided into dogs, question marks, stars, and cash cows by the development situation.

35

Based on the BCG Model, some Chinese scholars (Li & Sun, 2002; Li, 2002) divided the market into thin-dog market, child market, bright-star market, and golden-ox market entirely based on Chinese English. However, we consider it is more appropriate to translate them into dog market, child market, star market, and cash cow market. Acknowledgment The authors want to thank the English editor from Edtage who help us to modify English language problems in this article. Also, we thank Yada Noppakuntong who was an international graduate student from Thailand, she provided the newest data and information in this article. We still thank Zheshuai XU graduate student who gave us help in English too. Finally, we appreciate the grant (No: BZ201501) from the China-Asean Research Institute of Guangxi University. References Charles, R., Goeldner, J. R., & Brent, R. (1999). Tourism: Principles, practices, philosophies. John Wiley & Sons. Coshall, J. (2000). Spectral analysis of international tourism flows. Annals of Tourism Research, 27(3), 577e589. Fang, S. M., & Deng, L. J. (2014). Research on inbound tourism market of Hunan province: Based on the preference scale and competition state. Economic Geography, 34(12), 182e187. Fan, Y. F., Yan, J. P., & Shi, P. J. (2009). SSM analysis on the structure of inbound tourism market in Gansu province. Journal of Arid Land Resources and Environment, 23(2), 107e112. Gonzalez, S. (2011). Bilbao and barcelona ‘in motion’: How urban regeneration ‘models’ travel and mutate in the global flows of policy tourism. Urban Studies, 48(7), 1397e1418. Gregory, D., Johnston, R. J., Pratt, G., Watts, M., & Whatmore, S. (1988). The dictionary of human geography. Wiley-Blackwell. Hooper, J. (2015). A destination too far? Modelling destination accessibility and distance decay in tourism. GeoJournal, 80(1), 33e46. Huang, M. Y., & Wu, A. (2015). Research of Hunan inbound tourism market based on the market competitive state and its transfer model. Journal of Huaihua University, 34(8), 15e20. Lee, H., Guillet, B. D., Law, R., & Leung, R. (2012). Robustness of distance decay for international pleasure travelers: A longitudinal approach. International Journal of Tourism Research, 14(5), 409e420. Li, Y. J. (2002). The market competitive form and market dynamic developing model in tourism system. Economic Geography, 22, 219e222. Li, J. Y., & Sun, G. N. (2002). Tourism market competition model in China and its application. Resources Science, 24(6), 91e96. Liu, F. J., Chen, D. D., Zhu, J. H., et al. (2016). Structure and interaction of China's inter- provincial inbound tourism market: A 2-mode network analysis. Progress in Geography, 35(8), 932e940. Liu, C. J., & Gao, J. (2007). Analyses on inbound tourism market of Shanghai based on the competition state and the preference scale. Human Geography, 3, 73e77. Liu, J. S., & Ma, Y. F. (2012). Research on disparity of inbound tourism size and rank for cities in Henan province. Economic Geography, 32(6), 150e155, 172. Liu, Y. P., Zhang, Y., & Nie, L. L. (2012). Patterns of self-drive tourists: The case of nanning city, China. Tourism Management, 33(1), 225e227. Li, W. W., Yang, Y. C., & Zhao, S. D. (2012). Analyses on the pattern of Chinese FDI utilization and strategic choice based on the competition state. Economic Geography, 32(5), 23e29. Lü, S., Wang, Y. M., Gong, W., & Cheng, Y. (2006). A SSM analysis on the structure of tourism marketda case study on Shanghai oversea tourism market. Tourism Tribune, 21(11), 60e64. Mei, Y. (2014). Golden week tourism flow research based on tourism system model. Journal of Investigative Medicine, 62(8), 39e40. €, Y., & Kaivo-Oja, J. (2015). Integrating delphi methodology to some classical Myllyla concepts of the boston consulting group framework: Arctic maritime technology BCG delphi foresightda pilot study from Finland. European Journal of Futures Research, 3(1), 2. Racherla, P., & Hu, C. (2010). A social network perspective of tourism research collaborations. Annals of Tourism Research, 37(4), 1012e1034. Seung-Mook, C. (2015). An assessment of international tourism competitiveness and specialization of Korea, China and Japan with dynamic shift-share analysis. Journal of Tourism Industry Studies, 9(2), 78e94. Shen, B. J. (1999). On the basic study of tourism phenomenon. Tourism Tribune, 3, 58e60. Shen, J. S., Han, Y. X., & Li, Z. (2006). Thought on the urban planning of urban primacy index of Zhongshan. Planners, 22, 83e85. Shi, Z. Y., Zhou, B. H., & Shen, J. H. (2014). An empirical study on the influence factors of inbound tourism flow of Anhui province. Tourism Forum, 7(1), 51e58.

36

Y. Liu et al. / Tourism Management 64 (2018) 22e36

Skov-Petersen, H. (2001). Estimation of distance decay parameters e GIS based indicators of recreational accessibility. Bjørke J.t. & Tveite H.scangis Reseedings.ås. Tang, C. C., Song, C. Y., & Li, X. J. (2014). Analysis of disparity of inbound tourism rank and scale and its influencing factors for cites in Hebei province. Human Geography, 5, 155e160. Tang, L., Wu, J. F., Wang, J. Y., & Yang, X. J. (2012). Research on the spatial distribution and flow rules of Chinese inbound business tourist flows. Economic Geography, 32(9), 149e155. Taylor, P. J. (1971). Distance transformation and distance decay functions. Geographical Analysis, 3(3), 221e238.
th, G., D
To avid, L. D., & Vasa, L. (2014). The role of transport in European tourism flows. Acta Geographica Slovenica, 52(2), 311e320. Wang, Y. M., & Gao, Y. H. (2008). The tourism interaction between Shanghai and provinces in the Yangtze river valley. Acta Geographica Sinica, 63(6), 657e668. Wang, D., Tian, C., Lu, L., Wang, L., & Lew, A. A. (2015). Mechanism and HSR effect of spatial structure of regional tourist flow: Case study of beijing-Shanghai HSR in China. Acta Geographica Sinica, 70(2), 214e233. Wang, H., Zhang, M., Shi, Y., & Wang, L. (2013). Concentration and differentiation of tourism economy in island countries of China. Geographical Research, 32(4), 776e784. WEF. (2013). The travel & tourism competitiveness Report 2013. https://www. weforum.org/reports/travel-and-tourism-competitiveness-report-2013. WEF. (2015). The travel & tourism competitiveness Report 2015. https://www. weforum.org/reports/travel-and-tourism-competitiveness-report-2015. Wei, F. W., & Gan, Y. P. (2010). Analysis of inbound tourism market based on the market competitive state and its transference: A case study of Guangxi. Tropical Geography, 30(3), 311e316. Weng, J. (2008). Scale economy, product differentiation and the evolution for spatial structure of China's inbound tourism. Tourism Tribune, 23(6), 30e35. Wray, M. (2015). Drivers of change in regional tourism governance: A case analysis of the influence of the new south wales government, Australia, 2007-2013. Journal of Sustainable Tourism, 23(7), 990e1010. Wu, J. F., & Bao, H. S. (2005). Research on the distance decay of the tourist flow. Human Geography, 20(2), 62e65. Wu, B. H., Tang, J. Y., Huang, A. M., et al. (1994). A study on destination choice behavior of Chinese urban residents. Acta Geographica Sinica, 52(2), 97e103. Wu, D. D., Zhu, M. M., & Sun, G. N. (2015). Analysis on tourism market changes in Shandong based on market competitive state. Resource Development & Market, 31(2), 253e256. Yao, H., & Sun, G. N. (2008). Analysis on the transference of market competitive states of domestic tourism of the provinces in three zones in China. Areal Research and Development, 27(3), 53e56, 69. Zhang, Y., & Zhang, J. H. (2007). Analyses on the preference scale and competition on state of inbound foreign tourism market of Huangshan city. Human Geography, 2, 43e47. Zhou, Q., & Zhao, J. B. (2004). Study on market competition state and its transference of the regional economy in China. Economic Geography, 24(2), 167e171.

Professor Yaping Liu. Yaping Liu, holds Tourism Management in Ecology from Central South University of Forestry and Technology(China), and is a full professor at Business School of Guangxi University in Guangxi. [email protected]. Yaping Liu’ research interests are tourism economic impact and tourism management (destination management, development and protection of destinations), sustainable tourism and eco-tourism, the markets of inbound and outbound, etc.

Assistant Yinchang Li. Yinchang Li is an assistant and a doctoral candidate in Industry Economics of Guangxi University (China). [email protected]. His research interests are international tourism destinations (market of inbound and outbound, the behaviors of tourists), ecotourism, tourism economic impact etc.

International student Parnpree Parkpian. Parnpree Parkpian is a doctoral candidate in Industry Economics of Guangxi University (China), and he is an international student from Thailand. [email protected]. His interests are competitiveness of tourism market, destination management (development and protection), eco-tourism, ASEAN tourism etc.