SEDAAG annual meeting Savannah, Georgia; Nov. 22, 2011 Where is tourists next destination Yang Yang University of Florida
Outline Background Literature Model & Data Results Conclusion
Background The study of tourists destination choice is an important topic in tourism studies as it shed lights on the understanding of individual tourism demand. In most previous models investigating tourists destination choice, the traditional framework of origin-to-destination tourist movement is adopted, which assume that there is only one destination in tourists traveling itinerary (Eymann & Ronning, 1997; Nicolau & Más, 2008; Seddighi & Theocharous, 2002).
Background Although quite a bit of tourism literature has been devoted to investigating tourists destination choice, much less attention has been paid to this decision-making process in multidestination travel circumstance. As documented by many research (Hwang & Fesenmaier, 2003; Oppermann, 1992; Tideswell & Faulkner, 1999), multi-destination travel is common among tourists, especially for the long-haul tourists.
Background We apply a nested logit model incorporating spatial configuration factor to estimate tourists choice of subsequent destination from an on-site tourist survey in Nanjing, China. As specified in a three-level decision-making structure, a tourist is assumed to first consider whether to move on to the subsequent destination, then to select the type of subsequent destination, and finally to choose a destination within that particular type.
Literature Destination Choice of Tourists Many research adapted random utility theory, a classic microeconomic theory, to interpret tourist destination choice (Huybers, 2003a, 2003b, 2005; Nicolau & Más, 2008). It states that when tourists make decisions, they compare the utility of each choice. Another frequent referred economic theory is Lancaster s model on analysis of product characteristics (Lancaster, 1971). Papatheodorou (2001) suggested that this model is especially suitable to explain the movement of tourist flows in space and time.
Literature Multi-destination Travel As found by many research, a large proportion of tourists visit more than one destination during a single tour (Hwang & Fesenmaier, 2003; Lue, et al., 1993; Tideswell & Faulkner, 1999, 2003). Lue, et al.(1996) suggested four reasons for multi-destination travel: to satisfy various needs, meets the needs from other people within the traveling group, reduces risk and uncertainty and is cost-efficient and economic rational by to maximizing the use of money, time and efforts during a single tour (Tideswell & Faulkner, 1999).
Literature Factors contributing to the intention and extent of multidestination travel: supply-side factors and demand-side ones. The supply side factors indicate the opportunity to visit distinct destinations, while the demand side factors, such as individual tourists characteristics, reflect the desires to visit more than one destination (Wu & Carson, 2008). Some important factors: compatibility of each destination, VFR and business motivation, traveling distance, first-time tourists, organization pattern of tourists.
Literature Spatial Configuration and Tourism Demand Because of limitations to people s ability to process large amounts of information, a hierarchical information-processing strategy is likely to be used when the choice set is large. A common measurement of this spatial configuration effect is the accessibility of an alternative to all other alternatives, and its estimated coefficient suggests the competition/substitution patterns. In aggregate tourism demand research, many research emphasized the importance to take spatial configuration effects into consideration (Fesenmaier & Lieber, 1985; Hanink & Stutts, 2002; Kim & Fesenmaier, 1990).
Model and Data
1 st Stage 2 nd Stage 3 rd Stage Figure 2 Decision making process and specification of choice sets in nested logit model Tourist Home Subsequent Destination Destination Type A Destination Type B Beijing Shanghai Hangzhou Suzhou Huangshan Nantong Changzhou Yangzhou Wuxi Zhenjiang
P( t) P( m)* P( r m)* P( t r, m) and P( t r, m) N exp( X ) r k 1 exp( X ) t k P( r m) N exp( I Y ) r k 1 r r r exp( I Y ) k k k I N r where r k 1 ln exp( X ) and k exp( mim Zm) Pm ( ) 1 exp( I Z ) R m m m I ln exp( I Y ) m k k k k 1 P(m) the denotes the. probability of choosing to visit the next destination after leaving Nanjing, P(r m) is the probability of choosing type r destinations conditional on having chosen to continue traveling, and P(t r, m) is the probability of choosing destination t conditional on having decided to type r destination. I r, I k and I m are called inclusive value, reflecting the expected value of utilities derived from all alternatives within the nest.
Model and Data night (nights of stay in Nanjing), pastvisit (number of previous visit to Nanjing, 1=0; 2=1; 3=2-3; 4= 5 and above), age (age of tourist, 1=14 and below; 2= 15-24; 3=25-44; 4=45-64; 5=65 and above), motivation (1=vacation; 2=sightseeing; 3=VFR; 4=others), organization (1=by affiliations; 2=with families and friends; 3=by travel agents; 4=alone). distance1, which measure the distance from tourist s residence to Nanjing (in 100km). attraction (number of AAAA scenic spots in the destination) distance2 (distance from Nanjing to the subsequent destination alternative, in 100km), distance3 (distance from the subsequent destination alternative to residence, in 100km), j attraction j CD, which is competition destination effect specified as: CD t d d tj tj t d
Model and Data The dataset used in this research comes from the provincial domestic tourist on-site survey of Jiangsu province in 2007. Continuous data Table 2 Descriptive statistics of tourists profile Variable Mean Standard Deviation Minimum Maximum Cases distance1 5.388 4.460 0.445 38.497 3055 night 1.823 1.208 0.000 14.000 3055 Categorical data Variable value=1 value=2 value=3 value=4 value=5 pastvisit 45.696% 25.663% 13.813% 14.828% age 1.408% 15.385% 58.331% 24.255% 0.622% organization 6.743% 58.003% 9.460% 25.794% motivation 19.640% 66.187% 7.692% 6.481% For motivation, 1=vacation, 2=sightseeing, 3=VFR, 4=others. For organization patterns, 1=by affiliation, 2=with families and friends, 3=by travel agents, 4=alone.
Results First Stage All-sample Vacation Sightseeing VFR Other motivation night -0.298*** -0.550*** -0.268*** -0.223* -0.068 pastvisit -0.121*** -0.079-0.273*** -0.353** -0.235 age 0.282*** 0.019 0.322*** 0.417* 0.360 motivation=2-0.232** motivation=3-0.397** motivation=4-0.920*** organization=2 0.528** 0.589 0.125 2.081*** organization=3-0.269-0.178-0.561** -0.579* -0.275 organization=4-0.252-0.220-0.587** 0.287 Second Stage distance1 0.158*** 0.127 0.163*** 0.132* 0.013 Third Stage attraction 0.879*** 1.001*** 0.846*** 0.557 1.338 distance2-0.767*** -0.735*** -0.912*** -0.286*** -0.266 distance3 0.314*** 0.351*** 0.352*** -0.017-0.138* CD 0.004** 0.007** 0.004* 0.007 0.001
Results night is estimated to be negative, suggesting that a tourist with longer duration of stay in the previous destination is less likely to continue their tour to the next destination. (time constraints facing by tourists (Lew & McKercher, 2006)) pastvisit is also estimated to be negative and significant, suggesting that the more times the tourist has been to Nanjing in past, the less probability he/she will come to a subsequent destination after Nanjing. The estimated coefficient of age is positive and significant, indicating that older tourists are more likely visit subsequent destinations after leaving. Sightseeing tourists, VFR tourists and other-motivation tourists are less likely to visit subsequent destinations than vacation tourists. Tourists traveling with families and friends are more likely to continue their tour after visiting Nanjing than other three types of tourists.
Results A positive coefficient of distance1 suggests that, the longer the distance from tourist s residence to Nanjing, the more likely he/she will choose Type A destinations. A positive competition destination effect highlights the agglomeration effect among various destinations: destination could get extra benefits from clustering with neighboring destinations. A negative coefficient of distance2 suggests that the proximate destination to Nanjing are more likely to be chosen as tourists subsequent destination, while a positive coefficient of distance3 implies that tourists are more likely to choose to subsequent destinations which are distant from their residence
Results First Stage All-sample Vacation Sightseeing VFR Other motivation night -0.298*** -0.550*** -0.268*** -0.223* -0.068 pastvisit -0.121*** -0.079-0.273*** -0.353** -0.235 age 0.282*** 0.019 0.322*** 0.417* 0.360 motivation=2-0.232** motivation=3-0.397** motivation=4-0.920*** organization=2 0.528** 0.589 0.125 2.081*** organization=3-0.269-0.178-0.561** -0.579* -0.275 organization=4-0.252-0.220-0.587** 0.287 Second Stage distance1 0.158*** 0.127 0.163*** 0.132* 0.013 Third Stage attraction 0.879*** 1.001*** 0.846*** 0.557 1.338 distance2-0.767*** -0.735*** -0.912*** -0.286*** -0.266 distance3 0.314*** 0.351*** 0.352*** -0.017-0.138* CD 0.004** 0.007** 0.004* 0.007 0.001
Results For the first-stage decision making, night is estimated to be largest for vacation tourists. This because vacation tourists are more sensitive to time-constraints (Yang, Wong, & Zhang, 2011). Sightseeing and VFR tourists organized by travel agents are less likely to continue their trip to a subsequent destination. This can be explained by the fact that these tourists are constrained by the pre-arranged schedule by travel agents. By comparing the magnitudes of estimated coefficients of attraction, CD, and distance2, we find that vacation tourists are more attraction based while sightseeing tourists are more distance sensitive.
Results Simulation A simulation is carried out based on the scenario of an increase of one additional AAAA scenic spot in the subsequent destination. Home Beijing Shanghai Hangzhou Suzhou Huangshan Nantong Changzhou Yangzhou Wuxi Zhenjiang Beijing -0.001% 0.016% -0.003% -0.003% -0.005% -0.002% 0.000% 0.000% 0.000% -0.001% -0.001% Shanghai -0.026% -0.006% 0.257% -0.076% -0.059% -0.043% 0.002% -0.013% -0.012% 0.007% -0.014% Hangzhou -0.030% -0.008% -0.071% 0.313% -0.116% -0.047% -0.003% -0.013% -0.012% -0.003% -0.015% Suzhou -0.048% -0.011% -0.093% -0.108% 0.369% -0.072% -0.001% -0.004% -0.023% 0.027% -0.029% Huangshan -0.022% -0.007% -0.060% -0.059% -0.111% 0.289% -0.002% -0.008% -0.008% -0.015% -0.010% Nantong -0.058% -0.005% -0.026% -0.041% -0.028% -0.031% 0.426% -0.041% -0.057% -0.068% -0.070% Changzhou 0.013% 0.021% 0.271% 0.275% -1.192% 0.132% -0.009% 0.613% -0.053% -0.021% -0.065% Yangzhou -0.099% -0.008% -0.054% -0.073% -0.084% -0.044% -0.025% -0.087% 0.677% -0.167% -0.038% Wuxi -0.067% -0.007% -0.045% -0.052% 0.006% -0.041% -0.012% -0.026% -0.063% 0.387% -0.078% Zhenjiang -0.099% -0.008% -0.057% -0.063% -0.086% -0.046% -0.025% -0.082% -0.030% -0.163% 0.650%
Conclusion Based on the estimation result from nested logit model, it is found that, tourists age, motivation, organization pattern, duration of stay and past visit in previous destination influence the decision-making in the first stage: whether to move onto the subsequent destination. In the second stage, tourists decision regarding the choice of the type of subsequent destination is based their residence distance from the previous destination, and while in the third stage, destination attributes like number of attraction, competition destination effect, distance from the previous destination, and distance between the subsequent destination to residence, are significant. we split the sample to examine the model for tourists with different motivations, and carry out a simulation to look into the competition/substitution pattern between different subsequent destinations.
Comments and suggestions? Yang Yang yang.yang@ufl.edu