Determinants of Length of Stay: a general ordinal logit approach

Transcription

1 Determinants of Length of Stay: a general ordinal logit approach Yang Yang, Ivan Kevin, K.F, Wong School of Hotel and Tourism Management, the Hong Kong Polytechnic University Jie Zhang Institute of Tourism Studies, Nanjing University 引領亞洲酒店及旅遊業研究前沿

2 Outline Introduction Literature Review Background of Study Area Methodology Empirical Results Conclusion

3 Introduction The length of stay -- one of the most important indices measuring tourists demand and experience It shows the volume of tourism demand in destination. The long duration of tourists is considered to be beneficial to local economies due to the multiply effects (Archer & Shea, 1975)

4 Introduction Few studies have investigated on tourism demand function of individual tourist. By analyzing individual demand model, socio demographic attributes of tourist can be taken into consideration. Only a small number of papers have investigated the issue of length of stay, especially in recent years.

5 Introduction The purpose of this article is to identify the determinants of length of stay of individual tourists in Yixing, and to discuss the traveling behavior of Chinese domestic tourists. This paper will be one of the first attempts to study Chinese domestic tourists behavior and find out the difference between them and those of other countries

6 Introduction Five categories of determinants of length of stay are discussed respectively. They are economic variables, distance, social-demographical variables, past traveling experience, vacation characteristics and destination attributes.

7 Literature Review Economic Variables According to classic demand theory, consumers income and the price of product determine the amount that individual consumes. People with higher disposable income are more likely to consume more commodities.

8 Literature Review Distance In order to obtain the higher utility from a trip, a rational tourist is hypothesized to balance the proportion between the fixed cost and the varied cost (Smith, 1995).

9 Literature Review Social-demographic Variables Firstly, many studies argued that the relationship between the length of stay and age is non-linear. Furthermore, family life cycle plays a indispensable role in duration choice (Oppermann, 1995). Besides, the length of stay has been found to be dependent on national cultural background.

10 Literature Review Past Traveling Experience First, repeat visitors always stay longer in singer destination (Oppermann, 1997; Uysal & McDonald, 1989). Furthermore, as experience on international tourism increases, tourists are more inclined to stay longer in an international destination (Gokovali, Bahar, & Kozak, 2007)

11 Literature Review Trip Characteristics Different traveling motivations result in distinct length of stay. For different purpose, tourists participate in different activities and make different itinerary for trips, which require different duration (Andreu, Kozak, Avci, & Cifter, 2005; Kim & Prideaux, 2005; Seaton & Palmer, 1997)

12 Literature Review Destination Attributes As long as tourists make the decision for the longer stay, tourists become more aware of facilities and services at the destination where they are in. Also, the perceived attractiveness and image of the destination will stimulate the duration of stay (Gokovali, Bahar, & Kozak, 2007).

13 Authors Responders Model Operative Variables Mak, Moncur, and Yonamine (1977) Mak and Moncur (1979) Walsh and Davitt (1983) Silberman (1985) U.S. visitors to Hawaii Visitors in Hawaii Skiers in Aspen Visitors in Virginia Beach TSLS regression model Tobit model Stepwise leastsquares model 2SLS model Daily expenditures, air fare, flying time, hotel stay, income, traveling party attributes, marriage status, age, traveling purpose, past visits, visit in neighbor Price per day, accommodation type, income, available holiday time, age, marital status, education, group size, average annual rainfall, density of hotel rooms Average total variable cost per day, distance, annual household income, package plan, party size, skiing ability, state population, substitution. Price, accommodation, distance, number of trips, recreational activities, planned months, information channel, intention to return, repetition, certain perceptions, income, age, sex, marital status, number of children, employment situation, group size

14 Authors Responders Model Operative Variables Uysal, McDonald, and O'Leary (1988) Thunberg and Crotts (1994) Fleischer and Pizam (2002) Alegre and Pou (2006) National Recreation Survey Visitors in US Israeli seniors Tourists in Balearic Islands 2SLS model Binomial logit model Tobit model Binomial logit model Average total variable cost per person, distance traveled, skiing trips taken in the past 12 months, party size, crowding, prominence of manmade structures, prominence of non-recreational activities. Planning time, number of activities engaged in, number of adults in the travel party, number of children in the travel party, distance, purpose Age, income, vacation frequency Age, labour status, nationality, accommodation, type of board, number of trips, package holiday, revisitation rate, motivation, party size, daily price, total expenditure.

15 Authors Responders Model Operative Variables Gokovali, Bahar, and Kozak (2007) Alegre and Pou (2007) Gomes de Menezes, Moniz, and Cabral Vieira (2008) tourists in Bodrum, Turkey German and British tourists to Balearic Islands tourists in the Azores, Portugal Cox's and Weibull's regressions (Survival Analysis) Multinomial logit model Cox's regressions (Survival Analysis) nationality, income, international traveling experience, non-packaged vacation, reservation, past visits, attractiveness, standard of night life and entertainment, overall attractiveness and image, education, daily spending, annual abroad vacations, vacation type, accommodation type, hospitality level Age, profession, type of accommodation, type of board, number of previous trip, package holiday, size of the party, daily price of the stay, total spending on trip nationality, Azorean ascendancy, repeat visitation, motivation, travel party, type of flight, destination image and attitudes regarding environmental initiatives.

16 Background of Study Area Yixing, a city of Jiangsu Province in Eastern China, locates adjoin Anhui and Zhejiang Province, facing Taihu Lake in the east. Yixing connects to all three major metropolise in Yangzi Delta conveniently, about 180 km from Shanghai, 158 km from Nanjing and 168 km from Hanzhou. Furthermore, Yixing is abundant in tourism attractions.

17

18 Methodology Age SUMMARY OF THE EXPLANATORY VARIABLES Age of the reference person (in years) Distance Package tour Type of transportation Motivation Previous visitation Assessment of accommodation Assessment of transportation Assessment of tour guides Assessment of entertainment Assessment of shopping Distance between Yixing and the residence of tourist (in kilometers) 1 if the tourist is in package tour, 0 otherwise 1 if coach/bus, 2 if self-drive, 3 if airplane, 4. if train (reference) 1 if sight-seeing (reference), 2 if vacation, 3 if VFR, 4 if business, 5 if research and study 1 if no previous visitation, 2 if one to two times, 3 if three to four times, 4 if five times and above (reference) 1 if bad, 2 if neutral, 3 if good, 4 if fairly good, 1 if bad, 2 if neutral, 3 if good, 4 if fairly good, 1 if bad, 2 if neutral, 3 if good, 4 if fairly good, 1 if bad, 2 if neutral, 3 if good, 4 if fairly good, 1 if bad, 2 if neutral, 3 if good, 4 if fairly good,

19 Methodology DESCRIPTIVE STATISTICS OF CONTINUOUS VARIABLES Variable Mean Std. Dev. Min Max distance age assessment of accommodation assessment of transportation assessment of tour guides assessment of entertainment assessment of shopping

20 DESCRIPTIVE STATISTICS OF DISCRETE VARIABLES Variable Categories Frequency Percentage Length of stay Cumulated Percentage 1 day % % 2 days % % 3 days % % 4 days and above % % package tour Yes % % transportatio n No % % Coach/bus % % Self-drive % % airplane % % train % % motivation sight seeing % % vacation % % VFR % % business % % research % % study % % past No % % visitation 1 time to 2 times % % 3 to 4 times % % 5 times and above % %

21 Variable Model 1 Model 2 Model 3 Model 4 Model 5 distance ** ** ** ** ** age *** *** *** *** *** age_square 0.002*** 0.002*** 0.002*** 0.002*** *** package tour 0.765*** 0.663*** 0.706*** 0.701*** 0.682*** Transportation (df) 33.93(3)*** 32.59(3)*** 32.22(3)*** 32.10(3)*** 32.12(3)*** coach/bus *** *** *** *** *** self-drive *** *** *** *** *** airplane Motivation (df) 48.15(5)*** 53.66(5)*** 51.10(5)*** 51.74(5)*** 50.30(5)*** vacation 0.688*** 0.702*** 0.701*** 0.709*** 0.691*** VFR 1.696*** 1.979*** 1.826*** 1.862*** 1.892*** business 1.000** 0.917** 0.969** 0.968** 0.924** research 3.647*** 3.671*** 3.638*** 3.662*** 3.535*** study 1.555*** 1.748*** 1.676*** 1.687*** 1.635*** past visitation (df) 28.50(3)*** 28.55(3)*** 57.88(3)*** 27.53(3)*** 27.44(3)*** first-time visit *** *** *** *** *** 1 time to 2 times *** *** *** *** *** 3 to 4 times assessment 0.375** Variable included assessment of accommodation Education McKelvey and Scholarship & Zavoina's R2 assessment of transportation assessment of tour guides assessment of entertainment assessment of shopping

22 Leading Asia in Hospitality assessment and of Tourism accommodation 1 day vs.2 days and more Model day vs. 3 days and more distance * age *** age_squre 0.002*** 1-3 days vs. 4 days and more package tour 0.778*** 0.876** Transportation (df) 33.17(3)*** coach/bus *** self-drive *** airplane Motivation (df) 45.13(5)*** vacation 0.688*** VFR 1.687*** business 1.016** research 3.449*** study 1.596*** past visitation (df) 27.71(3)*** first-time visit *** 1 time to 2 times to 4 times *** 0.273* 0.569*** 0.887*** Pseudo R

23 0 Probability Results Distance 1 day 2 days 3 days 4 days and more

24 0 Probability Results Age 1 day 2 days 3 days 4 days and more

25 0 Probability Results fairly good good neutral bad Assessment of accommodation 1 day 2 days 3 days 4 days and more

26 variable changes SIMULATION RESULTS FROM THE MODEL probability changes 1 day 2 days 3 days 4 days and more distance: 100km to 300km % 1.070% 0.480% 0.220% age: 30 to % % % % assessment of accommodation: neutral to good % 1.060% 2.790% 2.550% non-package to package tour % 8.950% 3.800% 0.000% transportation: coach/bus to self-drive % % % % transportation: train to air 6.330% 2.070% % % motivation: sigh-seeing to vacation % 9.410% 3.700% 1.600% motivation: sight-seeing to VFR % % % 6.740% motivation: sight-seeing to business % % 6.220% 2.810% motivation: vacation to VFR % % 9.290% 5.140% past visitation: first-time to 1 time and 2 times past visitation: 1 time or 2 times to 3 or 4 times past visitation: 3 time or 4 times to 5 times or more % 7.820% 3.360% 1.490% % 8.700% 8.070% 4.440% % 0.620% 5.230% 3.940%

27 Conclusion The findings of this study show traveling distance, age (with the square of age), package tour, transportation, motivation, past visitation, assessment of accommodation to be influential factors. Based on the model, some marketing suggestions and implications can be given.

28 Comments are welcome