영화흥행에 있어서 구전의 영향력을 높이기 위해서는 구전의 효과를 분석하는 것이 중요하다. 그리고 영화의 흥행성공을 위해서는 구전활동과 더불어 사은품이나 경품, 가격할인과 같은 다양한 판촉활동을 병행하는 것이 필요하다. 본 연구는 개봉 전 후 영화의 구전효과와 영화의 판촉방식에 의한 소비자의 영화의 관람의도를 파악한 후 그 표본을 인구통계학적 방식으로 구분하고 그 집단 간의 차이 비교를 연구의 목적으로 한다. 기존 영화의 구전활동이나 판촉방식과 관련한 연구들이 이론적 근거에 치중한 반면, 본 연구에서는 현 시점에서 영화 제작사나 상영관, 그리고 배급사와 제휴사 등에 의하여 실제로 실시되고 있는 판촉방식을 사례로 들어 영화판촉과 관련한 사회현상을 이론화하였다는데 그 의의를 찾을 수 있다. 서울 시내 소재 B대학교 재학생 500여명을 대상으로 설문지를 배포하여 379부가 회수되었으며 불성실하게 응답한 10부를 제외하고 연구에는 총 369부의 설문지를 대상으로 연구를 진행하였다. Likert 5점 척도로 문항을 설정하고 상당한 의향이 있는 경우를 5점, 전혀 의향이 없는 경우를 1점으로 정하여 설문지를 제작하였다. 남녀 간, 전공계열 별 월평균 영화관람 횟수에 따라서 각각 T분석과 ANOVA분석을 실시하고 집단 간 비교분석을 시행한 후 사후분석을 실시하였다. 연구의 결과는 다음과 같다. 첫째, 영화의 판촉방식에 있어서 경품의 경우 남성에게 더 효과적이고 사은품의 경우 여성에게 더 효과적이었다. 둘째, 예술계열에 재학중인 학생들과 인문사회계열이나 자연계열에 재학 중인 학생들 간에 판촉방식에 따른 영화관람 의도에 차이가 있을 것이라는 예상과는 달리 그 차이를 찾아볼 수 없었다. 셋째, 판촉방식별로 구분해 보면 경품의 경우 월 평균 영화 관람 횟수에 따른 집단 간에 차이가 없었으나, 사은품 지급이나 가격할인의 경우 월 평균 영화 관람 횟수에 따른 집단 간에 영화관람 의도와 관련하여 차이를 발견할 수 있었다. 특히 구전효과의 경우 월 평균 1회 미만의 영화관람 집단과 1~2회 집단, 2~3회 집단 그리고 3회 이상과의 집단 간에 차이를 확인할 수 있었다. 넷째, 판촉 방식 중 가장 큰 효과가 있었던 것은 영화의 개봉 전 후 구전효과에 의한 관람의도였다. 따라서 영화의 제작사나 배급사에서 영화의 홍보 활동을 실시할 경우, 전문가 의견, 네티즌 평가 그리고 SNS 등 영화와 관련한 구전활동에 더욱 큰 관심을 두고 진행하여야 한다.
본 연구 시험포장의 토양은 식양질의 지산통으로 우리나라 논토양의 평균적인 화학적 특성과 비교하면 유효규산과 pH가 높았으며 유효인산과 유기물함량은 낮았다. 관행 및 유기재배구의 생육특성은 초장과 수장, 분얼수는 유의적인 차이가 없었으며 입모수의 경우 유의적인 차이를 보였다. 유기재배구에서 관행재배구에 비해 잡초발생이 많았으며 이는 수확작업 시 수확량의 손실에 지대한 영향을 미쳤으며 주요 초종으로는 뚝새풀과 벼룩나물이었다. 밀의 생리장해로서 도복의 발생은 유기재배구보다 관행재배구에서 더 많이 발생하였는데 이는 질소질비료의 시용량과 관련이 있는 것으로 보여진다. 병해충발생은 겉깜부기병, 붉은곰팡이병, 흰가루병, 잎마름병, 누른모자이크병, 노린재류, 진딧물이 발생하였으며 유기재배구에서 겉깜부기병과 붉은곰팡이병 다소 발생하였다. 관행과 유기재배구에서 수확한 밀알의 성분분석 결과, 단백질함량은 전남지방에서 생산되는 밀의 평균값보다 낮았고 회분함량은 높게 나타났으며 2019년과 2020년의 연차 간 밀알성분의 차이는 단백질, 지방의 함량은 증가하고 수분, 탄수화물함량은 감소하였으며 회분은 비슷한 함량을 보였다. 2019년의 밀 수확량은 유기재배구(559 kg/0.1 ha)가 관행재배구(532 kg/0.1 ha)보다 더 높은 수량을 보였지만 2020년에는 배수불량으로 인한 누른모자이크병과 잡초 발생으로 관행재배구는 10%, 유기재배구는 30%의 수확량 감수를 초래했다. 2019년의 밀 재배농가의 순수익은 관행재배구(46만원/0.1 ha)로 유기재배구(44만원/0.1 ha)에 비해 다소 높게 나타났으며 경영비는 유기재배구(16.2만원/0.1 ha)가 관행재배구(9.4만원/0.1 ha)에 비해 다소 높았는데 이는 유기재배에서 비료비와 종자비가 관행재배보다 높았기 때문이었다.
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.