문화관광축제 방문객의 평가속성 만족과 행동의도에 관한 연구 - 2006 광주김치대축제를 중심으로 - (The Effects of Evaluation Attributes of Cultural Tourism Festivals on Satisfaction and Behavioral Intention)
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- 마케팅과학연구
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- 제17권2호
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- pp.55-73
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- 2007
문화관광축제는 전국의 지역축제 가운데 광역시 도에서 추천한 축제 가운데 관광상품성이 크고, 경쟁력 있는 우수한 축제를 선정하여 지원하는 사업이다. 문화관광축제 종합평가계획(문화관광부, 2006)에 의하면 방문객의 만족도 평가, 전문위원 평가, 그리고 축제 개선실적 등을 감안하여 최우수축제, 우수축제, 유망축제, 예비축제로 선정되고 있다. 특히 예비축제를 제외한 문화관광축제는 공공부문의 사업비 지원을 받기 때문에 1,000여 개가 넘는 지역축제의 방문객 만족도 평가는 상호비교가 가능한 평가척도를 이용하여 종합평가분석이 이루어진고 있다. 이러한 견지에서 본 연구에서는 문화관광축제 공통평가속성이 방문객 만족과 사후행동의도에 미치는 영향관계를 파악하여, 향후 축제기획 시 방문객 만족도 제고와 지속가능한 문화관광축제로 선정되기 위한 시사점을 제시하였다. 본 연구에서는 이론연구를 통하여 문화관광축제 평가속성, 만족, 그리고 행위의도에 관한 변수를 도출하였으며, 2006 광주김치대축제 방문객을 대상으로 실증분석을 수행하였다. 문화관광축제 평가속성에 대한 요인분석을 통하여 홍보안내, 행사내용, 기념품 음식, 편의시설 요인을 도출하였으며, 축제방문객 만족과 행동의도와의 관계를 분석하였다. 연구모형을 통해 수립한 연구가설은 차이분석, 회귀분석, 공분산 구조분석 등을 통해 검증하였으며, 연구결과 모든 가설은 채택되었다. 향후 본 연구결과를 바탕으로 본 축제와 성격이 유사한 축제방문객 분석을 통한 비교연구를 기대한다.
본 논문은 지우재(之又齋) 정수영(鄭遂榮)(1743~1831)의 《한임강유람도권(漢臨江遊覽圖卷)》이 한양 근교 지역의 여러 곳을 선유(船遊)와 유산(遊山) 여행하면서 화가의 주관적인 시각이 잘 담긴 사적인 여행 기록이라는 점을 밝힌 연구이다. 이 작품은 정수영이 1796년 무렵, 각 지역을 여행하고 총 16m 길이의 긴 화권으로 표현한 것이다. 이처럼 독립된 경관으로 나뉜 작품을 긴 두루마리의 형태로 계속 이어 붙인 점은 《한임강유람도권》이 특별한 여행이 아닌 여행에서 마주한 주관적 경험을 담았음을 보여준다. 《한임강유람도권》은 장소에 따라 선유와 유산의 두 가지 여행 형태로 구분된다. 선유 여행 지역은 넓게 한강과 임진강 수계에 포함되며, 유산 여행 장소는 북한산과 관악산 일대, 도봉산 지역이다. 정수영은 넓은 범위의 장소를 한 번이 아닌 여러 번에 걸쳐서 여행했음이 확인된다. 특히 한강 수계의 장소에서 그려진 순서가 실제 위치와 다른 점은 이곳이 다른 시간대에 이루어졌을 것이라는 사실을 뒷받침해준다. 한강변 및 남한강변은 두 차례에 걸쳐 여행한 후, 제14장면까지 그렸다. 이 중 1차 선유 여행은 첫 장면에서 제8장면까지, 2차 선유 여행은 제9장면부터 제14장면까지인 점이 확인된다. 화권의 전체 길이 상 절반을 차지하는 양으로 한양에서 가까운 근교 지역이 많이 남겨진 것을 알 수 있다. 한강 수계의 1차 선유 여행은 잘 알려진 곳보다 자신의 여행 과정에서 눈으로 직접 마주한 풍경을 표현하였다. 용문산이나 청심루, 신륵사와 같이 여주 팔경에 속하는 곳도 그렸으나 흔히 명소를 표현하는 방식이 아닌, 자기의 눈에 들어온 잘 그려지지 않았던 장소를 수평시점으로 묘사하였다. 2차 선유 여행에서는 친구의 별업을 그리고, 친구들과 함께 한 순간을 담았다. 이에 더해 1차 여행에서 그린 청심루와 신륵사를 시점과 구도를 달리해 표현하였다. 정수영은 이미 그린 장소도 다른 시점과 각도에서 그리고자 했다. 한강 수계의 1, 2차 선유 여행은 길을 그대로 표현한 곳이 많아 현장감 있는 표현이 돋보이며, 배에서 경물을 수평적 바라보는 정수영의 시선이 잘 느껴진다. 임진강 수계에서는 영평천과 한탄강의 장소가 먼저 그려지고, 화권의 가장 마지막 부분은 임진강 물길에 있는 곳이 그려졌다. 이곳은 다양한 시점으로 표현되었는데, 여기서만 나타나는 특징적인 점이다. 정수영은 영평천에 있었던 장소에서 이어지는 물길의 흐름을 보여주기 위해 부감시점으로 표현했지만, 백운담에서 수평시점으로 바위의 모습을 나타낸 것은 바위에 대한 관심의 표현으로 보인다. 그가 보여주고자 하는 대상에 따라 자유롭게 시점을 사용했음을 알 수 있다. 한탄강변에 위치한 화적연은 주변의 모습과 바위가 한눈에 들어오도록 부감시점이 사용되었다. 임진강변에서는 우화정 주변의 모습이 부감시점과 파노라마 형식으로 잘 드러나며, 이보다 상류에 위치한 토산의 삼성대와 낙화암은 위로 올려다보는 시점[앙시(仰視)]으로 표현되었다. 유산 여행의 장소에서는 주로 정자와 암자가 그려졌다. 먼저 북한산의 재간정은 한강 수계의 선유 여행 경로와 이어지지 않으며, 다른 유산 여행과도 화권에서 분리되어 있기에 이전까지 정확한 장소가 밝혀지기 어려웠다. 필자는 한양 근교 중 재간정이라는 이름을 찾아 북한산 우이동구곡 중 한 곳과 손가장의 재간정으로 추정해보았다. 몇몇 기록들의 재간정에 대한 묘사가 비슷한 것은 두 곳 모두 북한산의 계곡에 위치했기 때문이다. 관악산 일대는 취향정과 일간정 두 정자와 관악산 줄기인 검지산이 그려졌다. 일간정은 관악산에서 잘 알려진 신위의 정자였으나, 정수영은 신위와 연관시키지 않고 객관적인 지리정보만 표기하였다. 취향정과 검지산에서는 담장이 묘사되어 있다. 정수영은 담장을 그리면서 <취향정>에서는 담장 안을 주제로, <검지산>은 담장 밖을 중심으로 다르게 그렸다. 도봉산에서는 망월암과 옥천암 두 암자를 담았다. 이 부분도 어느 곳을 그린 것인지 알려지지 않았으나 임천상의 『궁오집』을 통해 도봉산을 그린 것임을 확인할 수 있다. 다른 화가들이 그린 도봉산은 도봉서원과 만장봉을 부각시켜 드러낸 것에 반해 정수영은 암자 두 곳을 표현했다. <망월암>에는 영산전과 천봉탑비가 그려지고, <옥천암>에서는 대웅전이 정면으로 배치되었다. 정수영은 화면에 암자 건물과 함께 도봉산의 봉우리 모습을 그렸으나 봉우리의 명칭은 적지 않았다. 이를 통해 도봉산의 봉우리를 배경으로만 나타내고자 했던 것이 간취된다. 《한임강유람도권》은 정수영의 개인적인 시각이 잘 반영되어 있으며, 그가 지나온 여행의 흔적들을 담고 있는 기록이라는 점에서도 중요한 의미를 갖는 작품이다.
The objective of this research is to test the feasibility of developing a statewide truck traffic forecasting methodology for Wisconsin by using Origin-Destination surveys, traffic counts, classification counts, and other data that are routinely collected by the Wisconsin Department of Transportation (WisDOT). Development of a feasible model will permit estimation of future truck traffic for every major link in the network. This will provide the basis for improved estimation of future pavement deterioration. Pavement damage rises exponentially as axle weight increases, and trucks are responsible for most of the traffic-induced damage to pavement. Consequently, forecasts of truck traffic are critical to pavement management systems. The pavement Management Decision Supporting System (PMDSS) prepared by WisDOT in May 1990 combines pavement inventory and performance data with a knowledge base consisting of rules for evaluation, problem identification and rehabilitation recommendation. Without a r.easonable truck traffic forecasting methodology, PMDSS is not able to project pavement performance trends in order to make assessment and recommendations in the future years. However, none of WisDOT's existing forecasting methodologies has been designed specifically for predicting truck movements on a statewide highway network. For this research, the Origin-Destination survey data avaiiable from WisDOT, including two stateline areas, one county, and five cities, are analyzed and the zone-to'||'&'||'not;zone truck trip tables are developed. The resulting Origin-Destination Trip Length Frequency (00 TLF) distributions by trip type are applied to the Gravity Model (GM) for comparison with comparable TLFs from the GM. The gravity model is calibrated to obtain friction factor curves for the three trip types, Internal-Internal (I-I), Internal-External (I-E), and External-External (E-E). ~oth "macro-scale" calibration and "micro-scale" calibration are performed. The comparison of the statewide GM TLF with the 00 TLF for the macro-scale calibration does not provide suitable results because the available 00 survey data do not represent an unbiased sample of statewide truck trips. For the "micro-scale" calibration, "partial" GM trip tables that correspond to the 00 survey trip tables are extracted from the full statewide GM trip table. These "partial" GM trip tables are then merged and a partial GM TLF is created. The GM friction factor curves are adjusted until the partial GM TLF matches the 00 TLF. Three friction factor curves, one for each trip type, resulting from the micro-scale calibration produce a reasonable GM truck trip model. A key methodological issue for GM. calibration involves the use of multiple friction factor curves versus a single friction factor curve for each trip type in order to estimate truck trips with reasonable accuracy. A single friction factor curve for each of the three trip types was found to reproduce the 00 TLFs from the calibration data base. Given the very limited trip generation data available for this research, additional refinement of the gravity model using multiple mction factor curves for each trip type was not warranted. In the traditional urban transportation planning studies, the zonal trip productions and attractions and region-wide OD TLFs are available. However, for this research, the information available for the development .of the GM model is limited to Ground Counts (GC) and a limited set ofOD TLFs. The GM is calibrated using the limited OD data, but the OD data are not adequate to obtain good estimates of truck trip productions and attractions .. Consequently, zonal productions and attractions are estimated using zonal population as a first approximation. Then, Selected Link based (SELINK) analyses are used to adjust the productions and attractions and possibly recalibrate the GM. The SELINK adjustment process involves identifying the origins and destinations of all truck trips that are assigned to a specified "selected link" as the result of a standard traffic assignment. A link adjustment factor is computed as the ratio of the actual volume for the link (ground count) to the total assigned volume. This link adjustment factor is then applied to all of the origin and destination zones of the trips using that "selected link". Selected link based analyses are conducted by using both 16 selected links and 32 selected links. The result of SELINK analysis by u~ing 32 selected links provides the least %RMSE in the screenline volume analysis. In addition, the stability of the GM truck estimating model is preserved by using 32 selected links with three SELINK adjustments, that is, the GM remains calibrated despite substantial changes in the input productions and attractions. The coverage of zones provided by 32 selected links is satisfactory. Increasing the number of repetitions beyond four is not reasonable because the stability of GM model in reproducing the OD TLF reaches its limits. The total volume of truck traffic captured by 32 selected links is 107% of total trip productions. But more importantly, ~ELINK adjustment factors for all of the zones can be computed. Evaluation of the travel demand model resulting from the SELINK adjustments is conducted by using screenline volume analysis, functional class and route specific volume analysis, area specific volume analysis, production and attraction analysis, and Vehicle Miles of Travel (VMT) analysis. Screenline volume analysis by using four screenlines with 28 check points are used for evaluation of the adequacy of the overall model. The total trucks crossing the screenlines are compared to the ground count totals. L V/GC ratios of 0.958 by using 32 selected links and 1.001 by using 16 selected links are obtained. The %RM:SE for the four screenlines is inversely proportional to the average ground count totals by screenline .. The magnitude of %RM:SE for the four screenlines resulting from the fourth and last GM run by using 32 and 16 selected links is 22% and 31 % respectively. These results are similar to the overall %RMSE achieved for the 32 and 16 selected links themselves of 19% and 33% respectively. This implies that the SELINICanalysis results are reasonable for all sections of the state.Functional class and route specific volume analysis is possible by using the available 154 classification count check points. The truck traffic crossing the Interstate highways (ISH) with 37 check points, the US highways (USH) with 50 check points, and the State highways (STH) with 67 check points is compared to the actual ground count totals. The magnitude of the overall link volume to ground count ratio by route does not provide any specific pattern of over or underestimate. However, the %R11SE for the ISH shows the least value while that for the STH shows the largest value. This pattern is consistent with the screenline analysis and the overall relationship between %RMSE and ground count volume groups. Area specific volume analysis provides another broad statewide measure of the performance of the overall model. The truck traffic in the North area with 26 check points, the West area with 36 check points, the East area with 29 check points, and the South area with 64 check points are compared to the actual ground count totals. The four areas show similar results. No specific patterns in the L V/GC ratio by area are found. In addition, the %RMSE is computed for each of the four areas. The %RMSEs for the North, West, East, and South areas are 92%, 49%, 27%, and 35% respectively, whereas, the average ground counts are 481, 1383, 1532, and 3154 respectively. As for the screenline and volume range analyses, the %RMSE is inversely related to average link volume. 'The SELINK adjustments of productions and attractions resulted in a very substantial reduction in the total in-state zonal productions and attractions. The initial in-state zonal trip generation model can now be revised with a new trip production's trip rate (total adjusted productions/total population) and a new trip attraction's trip rate. Revised zonal production and attraction adjustment factors can then be developed that only reflect the impact of the SELINK adjustments that cause mcreases or , decreases from the revised zonal estimate of productions and attractions. Analysis of the revised production adjustment factors is conducted by plotting the factors on the state map. The east area of the state including the counties of Brown, Outagamie, Shawano, Wmnebago, Fond du Lac, Marathon shows comparatively large values of the revised adjustment factors. Overall, both small and large values of the revised adjustment factors are scattered around Wisconsin. This suggests that more independent variables beyond just 226; population are needed for the development of the heavy truck trip generation model. More independent variables including zonal employment data (office employees and manufacturing employees) by industry type, zonal private trucks 226; owned and zonal income data which are not available currently should be considered. A plot of frequency distribution of the in-state zones as a function of the revised production and attraction adjustment factors shows the overall " adjustment resulting from the SELINK analysis process. Overall, the revised SELINK adjustments show that the productions for many zones are reduced by, a factor of 0.5 to 0.8 while the productions for ~ relatively few zones are increased by factors from 1.1 to 4 with most of the factors in the 3.0 range. No obvious explanation for the frequency distribution could be found. The revised SELINK adjustments overall appear to be reasonable. The heavy truck VMT analysis is conducted by comparing the 1990 heavy truck VMT that is forecasted by the GM truck forecasting model, 2.975 billions, with the WisDOT computed data. This gives an estimate that is 18.3% less than the WisDOT computation of 3.642 billions of VMT. The WisDOT estimates are based on the sampling the link volumes for USH, 8TH, and CTH. This implies potential error in sampling the average link volume. The WisDOT estimate of heavy truck VMT cannot be tabulated by the three trip types, I-I, I-E ('||'&'||'pound;-I), and E-E. In contrast, the GM forecasting model shows that the proportion ofE-E VMT out of total VMT is 21.24%. In addition, tabulation of heavy truck VMT by route functional class shows that the proportion of truck traffic traversing the freeways and expressways is 76.5%. Only 14.1% of total freeway truck traffic is I-I trips, while 80% of total collector truck traffic is I-I trips. This implies that freeways are traversed mainly by I-E and E-E truck traffic while collectors are used mainly by I-I truck traffic. Other tabulations such as average heavy truck speed by trip type, average travel distance by trip type and the VMT distribution by trip type, route functional class and travel speed are useful information for highway planners to understand the characteristics of statewide heavy truck trip patternS. Heavy truck volumes for the target year 2010 are forecasted by using the GM truck forecasting model. Four scenarios are used. Fo~ better forecasting, ground count- based segment adjustment factors are developed and applied. ISH 90 '||'&'||' 94 and USH 41 are used as example routes. The forecasting results by using the ground count-based segment adjustment factors are satisfactory for long range planning purposes, but additional ground counts would be useful for USH 41. Sensitivity analysis provides estimates of the impacts of the alternative growth rates including information about changes in the trip types using key routes. The network'||'&'||'not;based GMcan easily model scenarios with different rates of growth in rural versus . . urban areas, small versus large cities, and in-state zones versus external stations. cities, and in-state zones versus external stations.