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Presenter: Jenny Yin-Chen Chen Advisors: Dr. Yen-Hsi Lee
Dr. Tzong-Shyuan ChenDecember 24, 2009
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Contents
I. Introduction
II. Literature Review
III. Methodology
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IV. Suggestions
Introduction
Research Background
Lukang
Statements of Problem
Purposes of Research
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Research Background
Distinct benefits of historical tourism include the potential of a clean industry and a valuable source of income and employment.
(Orbasli, 2000)
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Research Background
Interpretation allows visitors to generate a better understanding of the history and significance of events, people, and objects with which the site is associated.
(Alderson & Low, 1996)
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Research Background
Interpretation is one of the key factors to a sustainable tourism.
(Harris, Griffin, & Williams, 2002)
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Research Background
Nearly 91% of the citizens traveled at least once domestically in 2007, and the average number of trips per person was 5.57.
(R. O. C. Tourism Bureau, 2008)
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Research Background
The number of tourists who have visited historic sites in 2008 was only 5% of the total number of tourists who have visited the principal scenic spots in Taiwan.
(R. O. C. Tourism Bureau, 2009)
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Research Background
There are currently a total of 699 historic monuments and 767 historic buildings in Taiwan.
(Headquarters Administration of Cultural Heritage)
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Lukang
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Primary historic heritage 1
Tertiary heritage sites 6
Valuable heritage sites 7
Designated Heritage Sites in Lukang
Lukang
According to the survey of Visitors to the Principal Scenic
Spots in Taiwan by Month, more than 481,063 tourists
visited Lukang in 2008.
(R. O. C. Tourism Bureau, 2009)
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Statements of Problem
Only few of the past studies have examined the need for
interpretative services and the value of these services to
visitors in heritage sites.
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Statements of Problem
By probing the visiting patterns and the perceptions of
the tourists, more could be considered to increase
satisfaction of the tourists, and may further increase
revisitation.
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Purposes of Research
to use the contingent valuation method (CVM) to elicit
the willingness-to-pay (WTP) of the tourists for personal
interpretative service in Lukang, and to analyze WTP
determinants with the application of a double-hurdle
model
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Literature Review
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Interpretative Service
Contingent Valuation Method
Determinants of WTP
Double-Hurdle Model
Definition of Interpretative Service
Interpretation is an educational activity which aims to
reveal meanings and relationships to people about the
places they visit and the things they see, which in turn
improves the quality of visitor experience.
(Tilden, 1977)
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The Importance of Interpretative Service
The goal of interpretation is to increase visitor awareness,
promote learning, appreciation and understanding of
places so that tourists develop empathy towards heritage,
conservation, culture and landscape.
(Stewart, Hayward, & Devlin, 1998)
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The Importance of Interpretative Service
Interpretation services benefit both the heritage sites and
tourists and draw public support by enhancing visitors’
experiences and educating visitors in appropriate behaviors
to conserve the historical sites.
(Hall & McArthur, 1993)
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Types of Interpretative Service
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Personal / Attended
Non-personal / Unattended
guided walks
talks
presentations
drama
special events
activity programs
interpretative signs
interpretative brochures
exhibit center
audio guide
multi-media guide
interpretative trail
Importance of Personal Interpretative Service
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diverse audience needs
more interaction
entertaining and memorable
notice problems
Contingent Valuation Method
The contingent valuation method (CVM) is a standard
approach to measuring economic values of non-market
goods, such as recreation resources, wildlife, and
environmental quality goods.
(Hanemann, 1994; Lee & Han, 2002)
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Contingent Valuation Method
Elicitation techniques:
bidding game approach
payment card approach
dichotomous choice approach (DC)
open-ended elicitation technique
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Determinants of WTP
In travel expenditure studies, economic and socio-
demographic variables were commonly analyzed. Others
have incorporated travel-related variables, constraint
factors, and life cycle stages.
(Dardis, Soberon-Ferrer, & Patro, 1994; Hong, Fan, Palmer,
& Bhargava, 2005; Jang, Bai, Hong, & O’Leary, 2004;
Jang & Ham, 2009; Weagley & Huh, 2004)26
Double-Hurdle Model
Analysis of open-ended bids:
Ordinary least square (OLS) regressions
Tobit analysis
Double-hurdle model
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Double-Hurdle Model
Many researchers went through the process of the model
selection tests, and justified the double-hurdle model
from their findings.
(Angulo, Gil, & Gracia, 2001; Aristei, Perali, & Pieroni, 2008;
Gebremedhin & Swinton, 2003; Matshe & Young, 2004;
Saz-Salazar & Rausell-Koster , 2008)
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Double-Hurdle Model
Log likelihood statistics and Hosmer-Lemeshow statistics were
used to confirm that the double-hurdle model was good fit,
and the findings revealed differences in the variables
influencing travel participation and travel expenditure.
(Jang &Ham, 2009)
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Methodology
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Data Collection
Instrument
Estimation Methods
Data Analysis
Data Collection
Sampling size estimation formula
n =
n : sample size Z : 95 % confidence interval ( Zα/2 = 1.96 )
p : population proportion (½) e : tolerated error (5%)
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e2
Zα/22 p(1-p)
385
Data Collection
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Participants tourists who have visited Lukang (on-site)
Questionnaire 410 copies
Time 5 minutes
Sampling convenience sampling
Elicitation approach open-ended
Instrument
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Questionnaire survey
Part 4 Demographic Information
Part 1 Cognition of Personal Interpretative Services
Part 2 Lukang Traveling Experiences
Part 3 Willingness-to-Pay for Personal Interpretative Service in Lukang
Estimation Methods
Main reasons for zero responses:
the survey period is too short for participants to report any purchase (infrequency of purchase)
participants are not willing to pay due to personal preferences (abstention)
participants do not pay due to economic reasons (corner solution)
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Estimation Methods
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ordinary least square (OLS) regression
biased and inconsistent
estimates of the parameters
Estimation Methods
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Double-hurdle modelCragg (1971)
considers the possibility of zero outcomes in the second hurdle
two stages of estimation
two sets of variables
Tobit model
Tobin (1958)
all zero observations are interpreted as corner solutions
treats the decisions jointly
same set of variables
Heckman’s sample selection modelHeckman (1979)
there will be no zero observations in the second stage once the first stage selection is passed
two stages of estimation
two sets of variables
Data Analysis
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Double-Hurdle Model
1. The decision to pay for personal interpretative service (D):
Di* = Zi α ＋ ui , ui ~ N(0,1) (1a)
Di = 1 if Di* > 0
0 if Di* ≤ 0 (1b)
D* : latent selection variable Z i : vector of explanatory variables α : vector of parameters
u i : error term38
Double-Hurdle Model
2. The level of WTP value (Y):
Yi* = Xi β ＋ υi , υi ~ N(0, σ2) (2a)
Yi = Yi* if Di = 1 and Yi* > 0
0 otherwise (2b)
Yi : answer to the open-ended valuation question Xi : vector of explanatory variables β : vector of parameters υi : error term
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Double-Hurdle Model
Log-likelihood function:
(3)
ϕ(∙): standard normal density function
Φ(∙): standard normal cumulative distribution function
φ(∙): density function
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Suggestions
modification of the topic
information of the current personal interpretative service in Lukang
addition of tables and map
more literature on CVM and double-hurdle, and on determinants of WTP
specific sites of the survey
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Thank you for your attention!
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