Final defense

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Final defense

  1. 1. Tourist Demand and Willingness-to-Pay for Personal Interpretative Service: Application of the Bivariate Models Presenter: Jenny Yin-Chen Chen Advisors: Dr. Yen-Hsi Lee Dr. Tzong-Shyuan Chen May 25, 2010
  2. 2. Contents I. Introduction II. Literature Review III. Methodology IV. Results V. Discussions
  3. 3. Introduction Research Background Lukang Statements of Problem Purposes of Research
  4. 4. Research Background <ul><li>Distinct benefits of historical tourism include the potential of a clean industry and a valuable source of income and employment . </li></ul><ul><li>(Orbasli, 2000) </li></ul>
  5. 5. Research Background <ul><li>Interpretation allows visitors to generate a better understanding of the history and significance of events, people, and objects with which the site is associated. </li></ul><ul><li>(Alderson & Low, 1996) </li></ul>
  6. 6. Research Background <ul><li>Interpretation is one of the key factors to a sustainable tourism . </li></ul><ul><li>(Harris, Griffin, & Williams, 2002) </li></ul>
  7. 7. Research Background <ul><li>Nearly 91% of the citizens traveled at least once domestically in 2007, and the average number of trips per person was 5.57 . </li></ul><ul><li>(R. O. C. Tourism Bureau, 2008) </li></ul>
  8. 8. Research Background <ul><li>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. </li></ul><ul><li>(R. O. C. Tourism Bureau, 2009) </li></ul>
  9. 9. Research Background <ul><li>There are currently a total of 699 historic monuments and 767 historic buildings in Taiwan. </li></ul><ul><li>(Headquarters Administration of Cultural Heritage) </li></ul>
  10. 10. Lukang
  11. 11. Lukang
  12. 12. Lukang Primary historic heritage 1 Tertiary heritage sites 6 Valuable heritage sites 7 Designated Heritage Sites in Lukang
  13. 13. Lukang <ul><li>According to the survey of Visitors to the Principal Scenic Spots in Taiwan by Month, more than 481,063 tourists visited Lukang in 2008. </li></ul><ul><li>(R. O. C. Tourism Bureau, 2009) </li></ul>
  14. 14. Statements of Problem <ul><li>Only few of the past studies have examined the need for interpretative services and the value of these services to visitors in heritage sites . </li></ul>
  15. 15. Statements of Problem <ul><li>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 . </li></ul>
  16. 16. Purposes of Research <ul><li>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 bivariate models </li></ul>
  17. 17. Purposes of Research <ul><li>To determine: </li></ul><ul><li>tourists’ demand on personal interpretative service in Lukang </li></ul><ul><li>the determinants of tourists’ willingness-to-pay for personal interpretative service in Lukang </li></ul><ul><li>the determinants of the level of WTP value for personal interpretative service in Lukang </li></ul>
  18. 18. Literature Review Interpretative Service Contingent Valuation Method Determinants of WTP Double-Hurdle Model Infrequency of Purchase Model
  19. 19. Definition of Interpretative Service <ul><li>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. </li></ul><ul><li>(Tilden, 1977) </li></ul>
  20. 20. Definition of Interpretative Service <ul><li>Interpretation is a mission-based communication process that forges emotional and intellectual connections between the interests of the audience and the meanings inherent in the resource. </li></ul><ul><li>(National Association for Interpretation, 2009) </li></ul>
  21. 21. The Importance of Interpretative Service <ul><li>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. </li></ul><ul><li>(Stewart, Hayward, & Devlin, 1998) </li></ul>
  22. 22. The Importance of Interpretative Service <ul><li>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. </li></ul><ul><li>(Hall & McArthur, 1993) </li></ul>
  23. 23. Types of Interpretative Service <ul><li>guided walks </li></ul><ul><li>talks </li></ul><ul><li>presentations </li></ul><ul><li>drama </li></ul><ul><li>special events </li></ul><ul><li>activity programs </li></ul><ul><li>interpretative signs </li></ul><ul><li>interpretative brochures </li></ul><ul><li>exhibit center </li></ul><ul><li>audio guide </li></ul><ul><li>multi-media guide </li></ul><ul><li>interpretative trail </li></ul>Personal / Attended Non-personal / Unattended
  24. 24. Importance of Personal Interpretative Service <ul><li>Personal Interpretative Service </li></ul>diverse audience needs more interaction entertaining and memorable notice problems
  25. 25. Contingent Valuation Method <ul><li>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. </li></ul><ul><li>(Hanemann, 1994; Lee & Han, 2002) </li></ul>
  26. 26. Contingent Valuation Method <ul><li>Elicitation techniques: </li></ul><ul><li>bidding game approach </li></ul><ul><li>payment card approach </li></ul><ul><li>dichotomous choice approach (DC) </li></ul><ul><li>open-ended elicitation technique </li></ul>
  27. 27. Contingent Valuation Method <ul><li>Possible biases: </li></ul><ul><li>starting-point bias </li></ul><ul><li>sequencing effect </li></ul><ul><li>information effect </li></ul><ul><li>hypothetical bias </li></ul><ul><li>strategic bias </li></ul>
  28. 28. Determinants of WTP <ul><li>In travel expenditure studies, economic and socio-demographic variables were commonly analyzed. Others have incorporated travel-related variables, constraint factors, and life cycle stages. </li></ul><ul><li>(Dardis, Soberon-Ferrer & Patro, 1994; Hong, Fan, Palmer </li></ul><ul><li>& Bhargava, 2005; Jang, Bai, Hong & O’Leary, 2004; </li></ul><ul><li>Jang & Ham, 2009; Weagley & Huh, 2004) </li></ul>
  29. 29. Statistical Models <ul><li>Analysis of open-ended bids: </li></ul><ul><li>Ordinary least square (OLS) regressions </li></ul><ul><li>Tobit analysis </li></ul><ul><li>Bivariate models </li></ul>
  30. 30. Bivariate Models <ul><li>models that involve a separate process determining the zero-one discrete behavior from that determining the continuous observations </li></ul><ul><li>(Blundell & Meghir, 1987) </li></ul>
  31. 31. Double-Hurdle Model <ul><li>Many researchers went through the process of the model selection tests, and justified the double-hurdle model from their findings. </li></ul><ul><li>(Angulo, Gil & Gracia, 2001; Aristei, Perali & Pieroni, 2008; Gebremedhin & Swinton, 2003; Matshe & Young, 2004; </li></ul><ul><li>Saz-Salazar & Rausell-Koster , 2008) </li></ul>
  32. 32. Infrequency of Purchase Model <ul><li>Several researches, especially the ones focused on durable goods, have found the infrequency of purchase model to be a more suitable specification than Tobit or other models. </li></ul><ul><li>(Blisard & Blaylock, 1993; Blundell & Meghir, 1987; </li></ul><ul><li>Majima, 2008) </li></ul>
  33. 33. Methodology Data Collection Instrument Estimation Methods Data Analysis
  34. 34. Data Collection <ul><li>Sampling size estimation formula </li></ul><ul><li>n = </li></ul><ul><li>n : sample size </li></ul><ul><li>Z : 95 % confidence interval ( Z α/2 = 1.96 ) </li></ul><ul><li>p : population proportion (½) </li></ul><ul><li>e : tolerated error (5%) </li></ul>e 2 Z α /2 2  p (1- p ) 385
  35. 35. Data Collection Participants Tourists who have visited Lukang (on-site) Questionnaire 610 copies Time 5 minutes Sampling convenience sampling Elicitation approach Open-ended
  36. 36. Instrument <ul><li>questionnaire survey </li></ul>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
  37. 37. Estimation Methods <ul><li>Main reasons for zero responses: </li></ul><ul><li>the survey period is too short for participants to report any purchase ( infrequency of purchase ) </li></ul><ul><li>participants are not willing to pay due to personal preferences ( abstention ) </li></ul><ul><li>participants do not pay due to economic reasons ( corner solution ) </li></ul>
  38. 38. Estimation Methods ordinary least square (OLS) regression biased and inconsistent estimates of the parameters
  39. 39. Estimation Methods Double-hurdle model Cragg (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 model Heckman (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 Infrequency of purchase model Deaton and Irish (1984) the decision to pay is related to the amount willing to pay two stages of estimation two sets of variables
  40. 40. Data Analysis
  41. 41. Double-Hurdle Model <ul><li>1. The decision to pay for personal interpretative service ( D ): </li></ul><ul><li>D i * = Z i α + u i , u i ~ N (0,1) (1a) </li></ul><ul><li>D i = 1 if D i * > 0 </li></ul><ul><li>0 if D i * ≤ 0 (1b) </li></ul><ul><li> D* : latent selection variable </li></ul><ul><li> Z i : vector of explanatory variables </li></ul><ul><li> α : vector of parameters </li></ul><ul><li>u i : error term </li></ul>
  42. 42. Double-Hurdle Model <ul><li>2. The level of WTP value ( Y ): </li></ul><ul><li>Y i * = X i β + υ i , υ i ~ N (0, σ 2 ) (2a) </li></ul><ul><li>Y i = Y i * if D i = 1 and Y i * > 0 </li></ul><ul><li>0 otherwise (2b) </li></ul><ul><li>Y i : answer to the open-ended valuation question </li></ul><ul><li>X i : vector of explanatory variables </li></ul><ul><li>β : vector of parameters </li></ul><ul><li>υ i : error term </li></ul>
  43. 43. Double-Hurdle Model <ul><li>Log-likelihood function: </li></ul><ul><li>(3) </li></ul><ul><li>ϕ (∙): standard normal density function </li></ul><ul><li>Φ (∙): standard normal cumulative distribution function </li></ul><ul><li>φ (∙): density function </li></ul>
  44. 44. Infrequency of Purchase Model <ul><li>1. Participation decision: </li></ul><ul><li>PD * = Z i α + u i , u i ~ N (0,1) (4a) </li></ul><ul><li>D i = 1 if PD i * > 0 </li></ul><ul><li>D i = 0 if PD i * ≤ 0 (4b) </li></ul><ul><li>PD* : latent participation variable </li></ul><ul><li>Z i : vector of explanatory variables </li></ul><ul><li>α : vector of parameters </li></ul><ul><li>u i : error term </li></ul>
  45. 45. Infrequency of Purchase Model <ul><li>2. Expenditure decision: </li></ul><ul><li>Y i * = X i β + υ i , υ i ~ N (0, σ 2 ) (5) </li></ul><ul><li>if PD * > 0 and Y i * > 0 </li></ul><ul><li>= 0 otherwise (6) </li></ul><ul><li>Y i * : latent expenditure variable </li></ul><ul><li>X i : vector of explanatory variables </li></ul><ul><li>β : vector of parameters </li></ul><ul><li>υ i : error term </li></ul>
  46. 46. Infrequency of Purchase Model <ul><li>Log-likelihood function: </li></ul><ul><li>(7) </li></ul>
  47. 47. Results Descriptive Statistics Empirical Results
  48. 48. Descriptive Statistics
  49. 49. Descriptive Statistics Table 1 Socio-Demographic Statistics N % Gender female 333 61.0 male 213 39.0 Age 16~20 years old 50 9.2 21~30 years old 171 31.3 31~40 years old 161 29.5 41~50 years old 96 17.6 51~60 years old 53 9.7 61 or above 15 2.7 Education Level grade school or less 10 1.8 junior high school 17 3.1 senior high school 113 20.7 vocational school/university 337 61.7 graduate school or above 69 12.6
  50. 50. Descriptive Statistics Table 1 (Continued). Marriage unmarried 255 46.7 married 291 53.3 Occupation military/government/teacher 66 12.1 industry 96 17.6 business 78 14.3 service 98 17.9 freelance 22 4.0 agriculture 2 .4 housewife 43 7.9 student 90 16.5 unemployed 6 1.1 retired 15 2.7 others 30 5.5 N %
  51. 51. Descriptive Statistics Table 1 (Continued). N % Income 20,000 NTD or below 177 32.4 20,001~30,000 NTD 108 19.8 30,001~40,000 NTD 93 17.0 40,001~50,000 NTD 64 11.7 50,001~60,000 NTD 37 6.8 60,001~70,000 NTD 26 4.8 70,001~80,000 NTD 15 2.7 80,001 NTD or above 26 4.8 Number of Children 0 348 63.7 1 55 10.1 2 103 18.9 3 39 7.1 5 1 .2
  52. 52. Descriptive Statistics Note. N = 546 Table 1 (Continued). N % Residence north area 139 25.5 central area 306 56.0 south area 93 17.0 east area 3 .5 archipelagoes 1 .2 others 4 .7
  53. 53. Descriptive Statistics Table 2 Travel Experiences N % Domestically or Abroad (within three months) 0 times 86 15.8 1~2 times 216 39.6 3~4 times 160 29.3 5~6 times 42 7.7 7 times or above 42 7.7 Lukang never 37 6.8 once 61 11.2 twice 91 16.7 three times 68 12.5 four times 29 5.3 five times or above 260 47.6
  54. 54. Descriptive Statistics Table 3 Current Experience in Lukang N % Travel Companion alone 15 2.7 family or relatives 331 60.6 friend(s)/colleague(s)/classmate(s) 169 31.0 club activities 20 3.7 others 11 2.0 Transportation car 412 75.5 motorcycle 60 11.0 tourist coach 37 6.8 bus 25 4.6 others 12 2.2 Personal Interpretation Experience no 502 91.9 yes 44 8.1
  55. 55. Descriptive Statistics Note. N = 546 Table 3 (Continued). Understanding of Historic Monuments do not know at all 9 1.6 do not know much 111 20.3 Neutral 339 62.1 know some 77 14.1 know very much 10 1.8 Need of Interpretation do not need at all 5 .9 do not need 44 8.1 neutral 136 24.9 need 308 56.4 extremely need 53 9.7 N %
  56. 56. Descriptive Statistics Table 4 Perceptions Towards Historic Spots in Lukang N % Favorite Spot Lung Shan Temple 136 24.9 Tien Ho Temple 170 31.1 Lukang Folk Arts Museum 31 5.7 Yilou 5 .9 Lukang Old Street 176 32.2 Zhongshan Road 16 2.9 don't know the name 10 1.8 others 2 .4 Most Desirable Spot Lung Shan Temple 108 19.8 Tien Ho Temple 129 23.6 Lukang Folk Arts Museum 50 9.2 Yilou 20 3.7 Lukang Old Street 192 35.2 Zhongshan Road 23 4.2 don't know the name 17 3.1 others 7 1.3
  57. 57. Descriptive Statistics Note. N = 546 Table 4 (Continued). Interpretation Required Lung Shan Temple 178 32.6 Tien Ho Temple 127 23.3 Lukang Folk Arts Museum 79 14.5 Yilou 19 3.5 Lukang Old Street 110 20.1 Zhongshan Road 5 .9 don't know the name 24 4.4 others 1 .2 none 3 .5 N %
  58. 58. Descriptive Statistics Table 5 Cognition of Interpretation Minimum Maximum Mean Std. Deviation Interest 1 5 3.72 .868 Importance 1 5 4.15 .908 Knowledge 1 3 1.02 .134 Culture 1 3 1.02 .134 Preservation 1 3 1.15 .409 Entertainment 1 3 1.20 .473 Promotion 1 3 1.06 .261 Experience 0 5 2.76 2.008
  59. 59. Descriptive Statistics Table 6 Willingness-to-Pay WTP N % no 202 37.0 yes 344 63.0 Total 546 100.0
  60. 60. Descriptive Statistics Table 7 Frequency Table of WTP Price WTP (price) Frequency % 0 202 37.0 30 1 .2 50 17 3.1 80 1 .2 100 75 13.7 120 3 .5 150 25 4.6 200 64 11.7 225 1 .2 250 20 3.7 300 39 7.1
  61. 61. Descriptive Statistics Table 7 (Continued). 400 5 .9 450 2 .4 500 76 13.9 600 7 1.3 750 1 .2 800 3 .5 850 1 .2 900 1 .2 1000 2 .4 Total 546 100.0 WTP (price) Frequency % Average = 173 NTD
  62. 62. Empirical Results Table 9 Results of the First Hurdle of the Double Hurdle Model Variables Coefficient Std. Err. Z GEN 0.217446 0.13926 1.56 AGE2 0.251039 0.28316 0.89 AGE3 -0.08169 0.348207 -0.23 AGE4 0.135077 0.368522 0.37 AGE5 -0.0386 0.403974 -0.1 LOC1 0.272503 0.162924 1.67* LOC3 0.073215 0.1843 0.4 EDU3 0.2102 0.311975 0.67 EDU4 0.756491 0.30646 2.47** EDU5 0.926543 0.355189 2.61** MAR -0.01385 0.203433 -0.07 Participants who lived in north of Taiwan with educational level of vocational school/university, graduate school or above had a higher probability of WTP.
  63. 63. Empirical Results Note. * p < 0.1, ** p < 0.05 Table 9 (Continued). OCCU1 0.214026 0.237745 0.9 OCCU3 0.314069 0.180184 1.74* OCCU5 -0.0967 0.330041 -0.29 OCCU6 0.387278 0.248102 1.56 OCCU7 0.49827 0.281064 1.77* OCCU8 0.217078 0.282985 0.77 TRAN1 0.068221 0.25447 0.27 TRAN2 0.37904 0.325574 1.16 TRAN4 0.960131 0.438757 2.19** TRAN5 0.609615 0.537775 1.13 Variables Coefficient Std. Err. Z Those whose occupation is business or service, and housewife, and those who arrived by bus had a higher probability of WTP.
  64. 64. Empirical Results Note. * p < 0.1, ** p < 0.05 Table 9 (Continued). SER 0.079032 0.031477 2.51** VISIT 0.034587 0.040567 0.85 IMP 0.164878 0.067651 2.44** CUL -0.44197 0.50051 -0.88 NEED 0.335526 0.08059 4.16** Variables Coefficient Std. Err. Z Tourists who had more experiences of accepting personal interpretative service in the past, and who perceived the importance and need of personal interpretative service tended to be more willing to pay.
  65. 65. Empirical Results Table 10 Results of the Second Hurdle of the Double Hurdle Model Note. * p < 0.1, ** p < 0.05 Variables Coefficient Std. Err. Z GEN 0.110296 0.090606 1.22 AGE2 0.120599 0.189958 0.63 AGE3 0.051278 0.230249 0.22 AGE4 0.04588 0.24174 0.19 AGE5 0.114586 0.259569 0.44 LOC1 0.204132 0.10404 1.96* LOC3 0.176934 0.12384 1.43 EDU3 0.211445 0.259817 0.81 EDU4 0.058021 0.253388 0.23 EDU5 0.053082 0.27565 0.19 MAR -0.01462 0.129433 -0.11 North residential area had a positive influence on the level of WTP.
  66. 66. Empirical Results Note. * p < 0.1, ** p < 0.05 Table 10 (Continued). OCCU1 -0.0482 0.155819 -0.31 OCCU3 -0.08398 0.124956 -0.67 OCCU5 0.277577 0.249074 1.11 OCCU6 0.106041 0.164264 0.65 OCCU7 0.26202 0.222116 1.18 OCCU8 0.101301 0.197779 0.51 INC2 0.23121 0.147466 1.57 INC3 0.318127 0.153024 2.08** INC4 0.125124 0.172149 0.73 INC5 0.352857 0.20214 1.75* INC6 0.26721 0.16991 1.57 Variables Coefficient Std. Err. Z Participants with an income within the range of 30,001 to 40,000 and 50,001 to 60,000 NTD significantly increased the level of WTP.
  67. 67. Empirical Results Table 10 (Continued). Note. * p < 0.1, ** p < 0.05 TRAN1 0.329219 0.166975 1.97** TRAN2 0.198293 0.212468 0.93 TRAN4 0.228786 0.22798 1 TRAN5 0.48933 0.279619 1.75* SER 0.014218 0.020963 0.68 VISIT 0.00158 0.028086 0.06 IMP 0.032284 0.049078 0.66 CUL -0.29176 0.424457 -0.69 NEED 0.005786 0.059524 0.1 _cons 4.693261 0.655023 7.17 Variables Coefficient Std. Err. Z Participants who arrived by car or other transportations were willing to pay a higher price for personal interpretative service.
  68. 68. Empirical Results Table 11 Results of the First Stage of Infrequency of Purchase Model Note. * p < 0.1, ** p < 0.05 Variables Coefficient Std. Err. Z GEN 0.224688 0.092001 2.44** AGE2 0.381611 0.209885 1.82* AGE3 0.284645 0.243874 1.17 AGE4 0.443205 0.248853 1.78* AGE5 0.487823 0.277679 1.76* LOC1 0.17359 0.106316 1.63 LOC3 0.119741 0.125401 0.95 EDU3 0.246065 0.240421 1.02 EDU4 0.647336 0.236788 2.73** EDU5 0.775677 0.264394 2.93** Male participants within the age groups of 21 to 30, 41 or above, and with an educational level of vocational school/university, graduate school or above had a higher probability to pay.
  69. 69. Empirical Results Note. * p < 0.1, ** p < 0.05 Table 11 (Continued). MAR -0.37225 0.130534 -2.85** OCCU1 -0.07801 0.153848 -0.51 OCCU3 0.277023 0.135485 2.04** OCCU5 -0.15225 0.245432 -0.62 OCCU6 0.392794 0.192846 2.04** OCCU7 0.538586 0.212531 2.53** OCCU8 0.18732 0.204526 0.92 TRAN1 0.009606 0.195891 0.05 TRAN2 0.131724 0.236644 0.56 TRAN4 0.205951 0.276415 0.75 TRAN5 0.006335 0.335354 0.02 Variables Coefficient Std. Err. Z Marriage exerted a significantly negative effect on WTP probability. Occupation category of business or service (OCCU3), agriculture, unemployed, or retired (OCCU6), and housewife (OCCU7) had significant and positive effect on WTP probability.
  70. 70. Empirical Results Note. * p < 0.1, ** p < 0.05 Table 11 (Continued). SER 0.041032 0.022737 1.8* VISIT 0.030555 0.031351 0.97 IMP 0.161443 0.047804 3.38** CUL -0.30733 0.373036 -0.82 NEED 0.192137 0.058706 3.27** _cons -1.97148 0.605417 -3.26 Variables Coefficient Std. Err. Z Frequency of accepting personal interpretative services in the past, perception of both the importance and need of personal interpretative service to historic monuments had significant and positive impact on WTP probability.
  71. 71. Empirical Results Note. * p < 0.1, ** p < 0.05 Table 12 Results of the Second Stage of Infrequency of Purchase Model Variables Coefficient Std. Err. Z GEN 0.480192 0.16832 2.85** AGE2 0.783302 0.386828 2.02** AGE3 0.548014 0.439505 1.25 AGE4 0.85215 0.455667 1.87* AGE5 0.977989 0.511564 1.91* LOC1 0.412804 0.197708 2.09** LOC3 0.292994 0.230534 1.27 EDU3 0.655358 0.505374 1.3 EDU4 1.335538 0.487459 2.74** EDU5 1.520705 0.528414 2.88** Male tourists, within age groups of 21 to 30, or 41 and above, who lived in north of Taiwan, with educational level of vocational school/university or graduate school and above tended to be willing to spend more.
  72. 72. Empirical Results Table 12 (Continued). Note. * p < 0.1, ** p < 0.05 Variables Coefficient Std. Err. Z MAR -0.68706 0.227025 -3.03** OCCU1 -0.15971 0.306173 -0.52 OCCU3 0.459032 0.25485 1.8* OCCU5 -0.11516 0.538725 -0.21 OCCU6 0.784744 0.341254 2.3** OCCU7 1.175909 0.412248 2.85** OCCU8 0.413387 0.359515 1.15 INC2 0.093167 0.100233 0.93 INC3 0.180554 0.103918 1.74* INC4 0.057377 0.117494 0.49 INC5 0.212785 0.138182 1.54 INC6 0.14617 0.115584 1.26 Single tourists tended to be willing to pay more. Occupation category of business or service (OCCU3), agriculture, unemployed or retired (OCCU6), and housewife (OCCU7), and income of 30,001 to 40,000 NTD had positive association with WTP level.
  73. 73. Empirical Results Table 12 (Continued). Note. * p < 0.1, ** p < 0.05 TRAN1 0.226152 0.347258 0.65 TRAN2 0.356522 0.417446 0.85 TRAN4 0.468426 0.457214 1.02 TRAN5 0.338355 0.603904 0.56 SER 0.075679 0.043118 1.76* VISIT 0.050146 0.057882 0.87 IMP 0.338524 0.095928 3.53** CUL -0.78802 0.769336 -1.02 NEED 0.355363 0.111756 3.18** _cons -1.37613 1.194359 -1.15 Variables Coefficient Std. Err. Z Participants who experienced more personal interpretative service in the past, and those who perceived the importance and need of personal interpretative service in Lukang were willing to pay more.
  74. 74. Discussions Conclusions Limitations Suggestions
  75. 75. Conclusions <ul><li>single male </li></ul><ul><li>age groups of 21 to 30, or 41 and above </li></ul><ul><li>educational level of vocational school or university, and graduate school or above </li></ul><ul><li>business, service, agriculture, unemployed, retired, and housewife </li></ul>WTP probability WTP level
  76. 76. Conclusions <ul><li>a higher frequency of receiving personal interpretative service in the past </li></ul><ul><li>agreement with the statement that personal interpretative service is important to historic monuments </li></ul><ul><li>perception of the need of personal interpretative service in historic areas of Lukang </li></ul>WTP probability WTP level
  77. 77. Conclusions <ul><li>North residential area had a significant and positive effect on the WTP level , but not on the WTP probability. </li></ul><ul><li>Income of 30,001 to 40,000 NTD positively influenced the WTP level . </li></ul>WTP level
  78. 78. Conclusions <ul><li>The bivariate models yielded different sets of variables for the probability of WTP and the level of WTP. </li></ul><ul><li>Infrequency of purchase model indicated similar but more significant WTP determinants than the double hurdle model. </li></ul>
  79. 79. Limitations <ul><li>Lukang was the only study site. </li></ul><ul><li>The focus was merely on the demand and the determinants of personal interpretative services. </li></ul>include more historic sites focus on the development of a successful program and the standards of the quality
  80. 80. Suggestions <ul><li>promotion or marketing strategies </li></ul><ul><li>price of the services should be carefully calculated </li></ul><ul><li>standards or license for personal interpreters </li></ul>
  81. 81. <ul><li>Thank you for your attention! </li></ul>

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