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«VOLUM E 1 1, N UM B E R 1 I S SN 2 1 6 8 - 0 6 1 2 F L ASH DR I V E I S SN 1 9 4 1 - 9 5 8 9 ON L I N E T h e In s t it ut e f o r Bu s i n e s s an ...»

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Organized from greater to smaller effects, the market composition factors that affect the selection of accommodation by Thai tourists in the Bangkok area are namely: the aspect of the accommodation and quality of service; the aspect of site and distribution channel; the aspect of service personnel performance and physical characteristics; the aspect of service process, the aspect of marketing campaign, and the aspect of pricing. Every aspect of market composition greatly affected the selection of accommodation services by Thai tourists. This shows that the market composition factors affecting the selection of accommodation services vary. Fundamentally, there are 4 aspects; namely, product, price, channel of distribution and marketing promotion. However, for the service industry, market composition factors differ from market composition factors of general products, specifically, there must be emphasis on personnel, service process, and the physical surroundings. These three components are the main factors in service delivery. Therefore, the market composition factors of service are composed of the “7 P’s”: product and service, price, place, promotion of market, personnel, process of service and physical surroundings. The main factors in the aspect of accommodations and service most affect the selection of services by tourists are products, which are composed of form and characteristics, including the services related to those products. The most important part of the product aspect is determination to develop something to meets the needs of the consumer. The product market composition factors are given the most importance by tourists, and used to consider the selection of produce. If it is possible to create a product (which in this context means accommodations and services) that has quality, utility and value in the eyes of the client, the result will be successful sales of rooms.


1. Accommodation businesses and those involved should improve the method of determining the accommodation and service price to be standardized and clear, not raised and lowered according to the holiday or tourist season.

2. Accommodation businesses and those involved should improve all areas of service for the tourists, including manners, attention to the tourist and service ethic.

GCBF ♦ Vol. 11 ♦ No. 1 ♦ 2016 ♦ ISSN 1941-9589 ONLINE & ISSN 2168-0612 USB Flash Drive 430 Global Conference on Business and Finance Proceedings ♦ Volume 11 ♦ Number 1

3. Accommodation businesses and those involved should continually develop marketing strategy by distinguishing various forms of service in order to completely and directly meet the demands of the consumers.

4. At the accommodations, there should be safety standards for the life and possessions of the tourists to protect them from harm and loss of possessions.


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Khajit Korbdej. 1999. Hotel jobs: Room division : housekeeping work in hotels and other institutions. 4th Ed. Bangkok: Printique.

Theerasak Kambaanarak. 1998. Fun Psychology. 2nd Ed. Thammasat University Press. Bangkok hongchai Santiwong. 1999. Personnel management. Bangkok: Thai Wattana Panich.

Thanya Saehun. 1999. Introduction to Hotel Management.Nakhornpatom rajabhat Institute :


Nongnuch SriThananand. 2004. Basic Hoteling. 3rd Ed. Bangkok: Durakijbundit University Boonlert Jittangwatthana. 2005. Flight Business (1st Ed.). Bangkok: Press and design.

Preecha Dangroj. 2001. Tourism Industry into the 21st Century. Bangkok. Fire and Four Printing.

Pornthep Piyawattanamehtanakura (Editor). Uishiro. 1993 Handbook on Solving Sales and Service Problems. Bangkok :H. N. Groups, Ltd.

Pornsiri Thiwalannawong. 2033. Lecture Notes on Management Principles, Faculty of Management, Khonkaen University.

Wachirapron Lohachala. 2002. Satisfaction of Foreign Tourists in Selecting Accommodation Services in the Municipality of Mae Rim. Independent Research. Master of Arts, Tourism Industry Management,

Chiang Mai University Graduate College. Bangkok Website. 2012 [online]. Accessable at:

http://www.bangkok.go.th [accessed on 20 January 2012] Siriwan Serirat et al. 1998. Organizational Behaviour. Bangkok:Teerafilmm and Sytex Printing. The

American Marketing Association-AMA.) [online]. Accessible at :

http://www.idis.ru.ac.th/report/index.php?topic =263.0 [accessed on 20 January 2012.

Seri Wongmontha. 1999. Market Strategies: Market Planning. Bangkok: Theerafilm and Sytex Printing.

Anupont Kitjapanich. 1996. Professional Personnel manager. Bangkok: Human Heritage.

Kotler Philip & Aramstrong, Gary. 1996. Principles of Marketing 7th Ed. New Jersey: Prentice-Hall International.

Kotler Phillip. 1997. Marketing Management. New Jersey: Prentice - Hall.

Kotler, Philip. 2003. Marketing Management. 11th ed. Upper Sanddle River, New Jersey: Prentice-Hall.

GCBF ♦ Vol. 11 ♦ No. 1 ♦ 2016 ♦ ISSN 1941-9589 ONLINE & ISSN 2168-0612 USB Flash Drive 431 Global Conference on Business and Finance Proceedings ♦ Volume 11 ♦ Number 1

Stanton, William J., Buskirk, Richard H. & Spiro, Rosam L. 1991. Sale Management. ed.Homewood, IL:


GCBF ♦ Vol. 11 ♦ No. 1 ♦ 2016 ♦ ISSN 1941-9589 ONLINE & ISSN 2168-0612 USB Flash Drive 432 Global Conference on Business and Finance Proceedings ♦ Volume 11 ♦ Number 1

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This study examined the effect of traffic congestion costs on regional gasoline demand in South Korea based on gasoline demand function derived by a dynamic optimization. Considering endogeneity of gasoline price, 2SLS, GMM methods were compared with OLS. First, the estimated coefficients were as expected.

The coefficient of congestion costs was negative because the higher congestion costs incur the lower gasoline demand. Second, the higher traffic congestion costs were associated with the higher price elasticity. While consumers in the Metropolitan area in South Korea confront high traffic congestion costs, they seem to react flexible on price changes because they can use other choices of transportation (bus, subway, etc.) except for their own cars. Third, endogeneity was found, and 2SLS and GMM was more reliable than OLS. Also, price elasticity in the model without holding traffic congestion costs was overestimated otherwise. The findings imply that considering endogeneity of price and congestion costs may improve estimates and predictions of gasoline demand.

JEL CLASSIFICATION: Q41; D12; C26 KEYWORDS: Traffic Congestion Costs, Gasoline Demand, Price Elasticity, 2SLS, GMM


There are many studied of price and income elasticity on the gasoline demand. They study that the magnitude of demand responses as a results price changes and income changes. However, externalities such as traffic congestion costs have been overlooked for analyzing gasoline demand in South Korea. Traffic congestion costs(TCC) are type of external cost of transportation such as air pollution, noise, accident cost.

It includes loss of time and unnecessary vehicle running costs due to traffic jam. Therefore, traffic congestion cost is proxy variables as traffic congestion(Cho and Lee, 2008). In addition, Gasoline demand and distance per vehicle varies depending on traffic congestion cost. When magnitude of traffic congestion cost is increasing, households prefer use of public transportation to use of own car. Since most of gasoline demand in South Korea is consumed by utilizing own vehicles(Kim and Kim, 2011), Increase in traffic congestion cost will ultimately reduce distance per vehicle and gasoline consumption(Baltagi and Griffin, 1983). This paper has three major objectives. First, we analyze the effect of traffic congestion costs on gasoline demand. Since estimating the gasoline demand without effect of traffic congestion cost has omitted variables problem, this paper will compare gasoline demand whether TCC exists or not. Second, we show changes to price elasticity with varying traffic congestion costs. In addition, we compare price elasticity in Metro area which has high level of traffic congestion costs to price elasticity in Non metro area. Finally, considering that the endogenity problem of gasoline price, we use Instrumental Variables estimator such as 2SLS(Two Stage Least Square) and GMM(Generalized Method of Moments) and compare to the results of OLS. Because this endogeneity will leads to a biased parameter estimates, we contribute to estimate gasoline demand appropriately utilizing Instrumental Variables methods. Almost previous studies analyze price and income elasticity on gasoline demand based micro-foundation. They examined that effect of GCBF ♦ Vol. 11 ♦ No. 1 ♦ 2016 ♦ ISSN 1941-9589 ONLINE & ISSN 2168-0612 USB Flash Drive 433 Global Conference on Business and Finance Proceedings ♦ Volume 11 ♦ Number 1 population, industry structure and characteristic of households. The remainder of the paper is organized follows: Section 2, we introduce gasoline demand function by a dynamic optimization, and provide the methodology. Section 3, we shows that results is estimated. Section 4, we concludes.

Gasoline Demand and TCC Gasoline consumption is composed ot three separate determined. There are the relationship is given by equation (1) (Batagi and Griffin, 1983; Medlock and Soligo, 2002).

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Where is gasoline consumption, is distance(kilometer) per vehicle, is number of vehicle. Assume that the energy efficiency is given by current technology, households consume gasoline for using vehicle.

This study examined the effect of TCC on gasoline demand in South Korea based on gasoline demand function derived by a dynamic optimization. we assume utility to be concave twice–differentiable function of the gasoline( ) and all the other goods( ). and The consumer‘s problem is therefore formulated

as(Medlock and Soligo, 2002):

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where is all the other goods, is financial saving bearing the rate of return R, is purchases of vehicle stock, , is gasoline price, is vehicle price, is wage income, is δ depreciates rate about vehicle stock. It is equivalent that consumers choose gasoline to maximize utility to that choose vehicle demand and distance per vehicle, since equation (1) is defined. When first order condition for a maximum for this cost, (Medlock and Soligo, 2002).

consumer’s problem is derived, optimal vehicle demand and distance per vehicle derived is affected User

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Where the star denotes an optimal value, in equation (5), user cost( ) of vehicles is composed two part:

one is gasoline price( ), The other is rental price of vehicle( − (1 − δ⁄1 + )) We focus on first term at the right-hands side, ( ⁄ ). It consists of gasoline price( ), optimal distance( ), and ∗ ∗ gasoline efficient( ) Because of traffic congestion costs(TCC) due to road traffic congestion when traveling on the road by vehicle, TCC are closely associated with the distance(kilometer) per vehicle.

Furthermore, with increasing TCC, households may find substitutes of vehicle such as, bus, subway etc.

Since gasoline consumption in South Korea is consumed by using car(Kim and Kim, 2011), Increasing

TCC will ultimately leads to reduce the gasoline consumption:

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rates, ( ), is proxy variables about rental price( − (1 − δ⁄1 + )) (It is two reasons that we use interest rates as proxy variables about rental price. First, rental price of vehicle is defined opportunity cost when households purchase vehicle(Diewert, 1974; Medlock and Soligo, 2002). Second, rental price of vehicle is theoretically equivalent to the marginal product revenue of vehicles(Felipe and McCombie, 2007).) user cost (8)-(9) plus the full set of first order conditions for consumer’s problem can be solved vehicle demand and demand for distance travelled by car(Medlock and Soligo, 2002).

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Where is total income as households wage income plus assets, equation (10) and equation (11) is implied vehicle demand and demand for distance that is not included TCC. Eq. (12) and eq. (13) is expressed as vehicle demand and demand for distance affected by TCC. Eq. (10)-(13) is depends on gasoline price( ), interest rate(( )), wealth( ), regardless of the presence TCC. We focus to find out how TCC affect demand for distance and vehicle, because they are influenced by TCC in different ways. Both vehicle( ) and distance( ) are affected by interaction term between TCC and gasoline price ( ( )). However, demand for distance( ) is affected by not only interaction term but also a direct impact on TCC. Because, we see equation (6), TCC directly reduces the real distance. By equation (1)’s identity, equation (10)-(13)

allows us to write a general function describing gasoline demand as:

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