• Asia Marketing Journal
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• 제12권2호
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• pp.155-175
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• 2010

본 연구는 관계혜택과 브랜드 동일시에 관한 탐색적 연구로서 충성도에 긍정적인 영향을 미치는 것으로 알려져 온 관계 혜택과 브랜드 동일시를 함께 고찰하려는 데에 목적이 있다. 관계 혜택과 브랜드 동일시는 각각 충성도와 유의한 관계가 있는 것으로 잘 알려져 왔으나, 그들 간의 관계를 함께 살펴본 연구는 거의 없는 실정이다. 한편으로는 카드산업에서 관계혜택이 중요한 전략적 요소인 동시에 최근에는 브랜드 개성과 이미지를 카드에 연결시키려는 시도를 하고 있다. 따라서 본 연구에서는 관계 혜택과 충성도와의 관계를 브랜드 동일시가 매개할 것으로 보고 그 영향을 탐색하였다. 결과적으로 관계혜택 차원, 즉 확신적 혜택과 특별대우혜택이 개인적 동일시와 사회적 동일시에 영향을 미치고, 개인적 동일시가 충성도에 영향을 미치는 것으로 나타났으며, 특히 확신적 혜택은 충성도에 직접적인 영향도 있는 것으로 나타났다.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• 제1권1호
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• pp.56-78
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• 1998

연구목적: 만성 B형 간염의 간조직 소견은 진단 및 예후의 평가 뿐아니라 치료여부의 결정 및 치료효과의 평가를 위해서 중요하고, 이는 좀 더 객관적, 구체적, 포괄적으로 새롭게 분류할 필요성이 있다. 우리나라 소아의 만성 B형 간염의 빈도는 높을 것으로 추정되는데 아직 이에 대한 병리조직학적 고찰은 거의 없는 상태이다. 이에 저자들은 점수로 평가한 최근의 반정량적 분류 방법(Ludwig 분류법)을 전통적으로 이용되어온 De Groute의 분류법과 비교하여 연관성을 밝히고, 반정량적 분류법이 좀 더 객관적으로 예후를 예견하고 형태학적 변화를 감시하는 유용한 방법임을 소개하고자 하였다. 대상 및 방법: 서울대학교병원 소아과에 입원하여, 간조직 생검을 받은 만성 B형 간염 환아 99명을 대상으로 병리조직 소견과 병록지 고찰을 시행하였다. B형 간염 표식자로는 HBsAg, anti-HBs, HBeAg, anti-HBe, anti-HBc(IgG, IgM), HDV를 방사면역측정(radioimmunoassay)을 이용하여 검사하였다. 간생검조직은 전통적인 De Groute의 병리조직학적 분류법과 병리조직학적 변화를 문맥강 및 문맥강 주변의 염증(Portal/Periportal activity), 간소엽 내 염증(Lobular activity), 섬유화(Fibrosis)로 나누어 각각의 정도를 0에서 4로 점수화(Numerical scoring)한 Ludwig의 체계에 따라 분류하였다. 두가지 분류법 간의 연관성과, 병리조직 소견에 따른 빈도, 연령 및 성별 분포, 임상적 특징, 생화학적 및 혈청학적 검사 소견을 비교 조사하였다. 또한 간세포암 5례는 따로 분류하여 임상적 고찰을 시행하였다. 결 과: 1) 총 99례 중 남아 83례, 여아 16례였고, 연령별 분포는 1년 5개월에서 16년 4개월까지였으며, 평균연령은 9.4세였다. 병리조직형별 연령 및 성별분포에는 차이가 없었다. 2) 병리조직형별 분포는 정상 조직 소견 2례, 만성 소엽성 간염 2례, 만성 지속성 간염 22례, 경도 만성 활동성 간염 40례, 중등도 만성 활동성 간염 19례, 고도 만성 활동성 간염 1례, 간경변을 동반한 만성 활동성 간염 7례, 간경변증 1례, 간세포암 5례였다. 두가지 분류법은 높은 연관성을 보였으나 간경화의 경우는 연관성이 떨어져서, 간조직내 염증과 섬유화는 따로 분리하여 평가하거나, 섬유화의 중량이 필요함을 알 수 있었다. 3) 간세포암을 제외한 94례 중 66례(70%)가 신체검사에서 우연히 발견된 경우였으며, 2례는 전신 부종이 주소로서 B형 간염과 관련된 막성 사구체 신염이 동반되어 있었다. 그 외는 비특이적인 간염증상을 주소로 내원하여 진단되었다. 내원시 임상 증상으로는 이피로성, 식욕부진, 오심, 구토, 복통, 복부팽만, 체중감소, 발열, 진한 소변색, 황달, 비출혈, 부종, 혈뇨 등이 있었고, 이학적 소견으로는 간장비대, 비장비대, 우측 상복부통이 있었다. 병리조직형별 임상증상, 이학적 소견 및 그 빈도에는 차이가 없었다. 4) 총 99례 중 가족력상 어머니에 B형 간염의 병력이 있는 경우가 54례, 아버지가 12례, 형제들의 경우가 25례 있었으며, 그 외의 동거 가족에서 가족력이 있는 경우는 17례 있었고, 가족력이 없는 경우는 28례였다. 가족 중 간경화가 있는 경우는 9례였고, 간세포암도 2례에서 있었다. 어머니가 만성 활동성 간염으로 사망한 례가 1례 있었고, 형제가 전격성 간염으로 사망한 경우도 1례 있었다. 병리조직형별 가족력의 차이는 없었다. 5) 간기능 검사소견을 보면, AST와 ALT의 평균은 각각 $151{\pm}158$ U/L, $215{\pm}221$ U/L이고, 병리조직형별로는 조직 염증소견이 심할수록 유의하게 높은 것을 알 수 있었다. 그러나 병리조직형별로 중복되는 부분이 많아 AST, ALT치로 조직형을 예측하는 것은 어려웠다. Ludwig의 분류와 간기능 검사치와의 관계를 보면, 혈청 알부민은 섬유화가 진행할수록 유의하게 감소하는 것을 관찰할 수 있었고, ALT, AST는 간조직내 염증 및 섬유화가 심할수록 유의하게 증가하는 것을 관찰하였다. 6) Prothrombin time은 조직형이 심할수록 유의하게 길어졌고, 특히 간경화가 진행될수록 나빠지는 것을 관찰할 수 있었다. APTT(activated partial thromboplastin time)도 조직형이 심할수록 길어지는 것을 관찰할 수 있었으나, 통계적 유의성은 없었다. 반정량적 방법에 의한 분류와의 관계를 보면, Prothrombin time과 APTT는 간조직내 염증 및 섬유화가 심할수록 길어지는 것을 관찰하였으나, 통계적 유의성은 없었다. 7) IgM anti-HBc는 검사가 이루어진 80명 중 1명에서만 양성이었고, HDV는 검사가 이루어진 83명 중 양성인 경우는 한 례도 없었다. HBeAg은 간생검 당시 81례에서 양성이였고, 이 후 평균 $41.8{\pm}31.3$개월간의 추적 중에 HBeAg 음전을 보인 경우는 50례가 있었고, 1년 음전율은 37%였다. 병리조직형별로는 조직형이 심할수록 음전시까지의 기간이 짧은 것을 관찰할 수 있었다. 8) 간세포암 5례의 남녀 분포는 남아 4례, 여아 1례이고, 평균연령은 $10.4{\pm}3.1$세(7세~13세 6개월)였다. 모두에서 HBsAg양성, HBeAb양성과 어머니의 만성 B형 간염의 가족력이 있었다. 간세포암 주변 조직 소견은 1례를 제외하고는 모두에서 간경화의 소견을 보였다. 추적 중 혈청 AFP의 증가로 조기발견된 1례는 종양절제술 후 항암요법으로 추적 중에 있고, 그 외에는 모두 수술 후 항암요법 혹은 방사선요법을 병행하여 시행했으나 사망하였다. 결 론: 만성 B형 간염의 간손상 정도는 임상증상이나 생화학적 검사소견으로 평가할 수 없었고, 간조직검사에 의한 병리조직학적 분류가 필요하였다. 이는 De Groute의 분류와 잘 연관되는 반정량적 Ludwig 방법에 의해 좀 더 포괄적, 구체적, 객관적으로 분류할 수 있었고, 후자는 예후를 예견하고, 형태학적 변화를 감시하며, 통계적으로 분석하는 데 유용한 방법임을 시사하였다.

• 대한교통학회:학술대회논문집
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• 대한교통학회 1995년도 제27회 학술발표회
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• pp.101-113
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• 1995

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.