최근 많은 기업들이 치열한 경쟁에서 생존하기 위해 개별 고객들에게 초점을 맞춘 전사적이고 체계적인 고객관계관리에 전력을 기울이고 있다. 수익성 높은 대부분 기업들의 성공비결은 복합적이겠지만, 고객지향적 사고에의 신속한 적응이 중요한 부분을 차지하고 있다. 고객관계관리 기법 및 운용철학은 고객을 올바르게 이해하는데서 그치지 않고 고객행동을 사전적으로 예측하여 고객요구에 부응한 제품과 서비스를 제공하는 것만이 치열한 경쟁환경에서 생존함과 동시에 거듭된 성장을 이루는 유일한 해결책임을 강조한다. 고객관계관리는 데이터베이스마케팅과 같은 조직내 실무자 중심의 관점과 접근이 아니라 최고경영자의 마케팅 관점의 경영철학 구현을 통한 전사적이고 조직적인 참여가 이루어져야 한다. 그럼에도 불구하고 많은 기업들이 고객관계관리 기법을 도입하고 구축하는 과정에서 이러한 점을 간과해 왔으며 그 결과, 고객관계관리를 통해 수익성을 높인 기업이 있는 반면에 고객관계관리에 엄청난 비용만을 투입하고 별다른 성과를 거두지 못한 기업들도 다수이다. 본 연구는 CRM구축 및 실행과정에서의 성공요인을 기존 연구와 달리 마케팅적 관점에서 발견해 내고 있다. 시장지향성과 고객지향성이라는 마케팅 철학에서부터 고객정 보지향성과 핵심고객지향이라는 실무적 개념까지 포함해서 마케팅적인 관점에서의 성공적 CRM구축을 위한 선행요인을 발견하고, 이러한 요인들이 마케팅관점의 관계품질과 실무적인 CRM성과에 어떤 영향을 미치는지를 분석함과 동시에 관계품질과 CRM성과 간의 관계의 강도까지 실증적으로 분석해 보았다. 경험적 분석 결과 본 연구에서 구축한 마케팅관점의 CRM선행요인들 중에서 일부 요인을 제외하고는 대체적으로 관계품질 및 CRM성과를 높이는데 상당한 기여를 하고 있음이 확인되었으며, 영향관계의 정도에는 어느 정도 차이가 있음이 확인되었다. 또한 관계품질과 CRM성과 및 세부적 개념구성요인들 간에 매우 높은 정(+)의 관계가 존재함을 확인했다. 이는 CRM의 최종 성과를 달성하기 위해서 CRM구축 및 실행이후에 고객만족과 고객신뢰라는 개념적 연결고리를 강화함과 동시에 이러한 관계품질이 고객유지와 고객점유 정도의 향상으로 이어지도록 하는 창조적 전술개발이 요구됨을 의미한다. CRM을 구축 및 실행하는 대부분의 기업들이 조급하게 재무적인 성과를 기대하는 경향이 있는데, CRM은 마케팅철학을 포함하는 장기적인 경영활동임을 주지해야 한다. 기존의 많은 연구들이 취하고 있는 연구맥락에 근거해서 기술적인 시스템만을 갖추었다고 하여 단기적인 성과를 바라는 것은 오히려 비용의 낭비만을 초래 할 수 있음에 주목해야 한다. 본 연구결과를 바탕으로 CRM의 성공적 구축을 통해 관계품질을 강화하는 것에 대한 전략적 통찰을 제공함과 동시에 실질적인 CRM성과를 달성하기 위한 마케팅 관점의 연결구조를 어떻게 효율적으로 강화할 수 있을 것인가에 대한 학술적이고 실무적인 시사점을 도출했다.
본(本) 연구(硏究)는 강원도(江原道) 새마을 '소' 임간공동방목사업(林間共同放牧事業)의 일환(一環)으로 81년도(年度)에 개설(開設)된 105개(個)의 공동방목장(共同放牧場)과 '82년도(年度)에 개설(開設)된 103개(個)의 공동방목장(共同放牧場)의 경영실태와 분석(分析)된 문제점(問題點) 그리고 개선방안(改善方案)에 관한 연구결과(硏究結果)를 요약(要約)하면 다음과 같다. 1. 공동방목(共同放牧) 사업(事業)의 효과 1) 방목기간중(放牧期間中) 1 일(日) 평균(平均) 증체량은 0.46kg으로서 농가(農家) 관행사육(慣行飼育)의 0.33kg보다 높았다. 2) '82년도(年度) 208개(個) 공동방목장(共同放牧場)의 방목기간(放牧期間)(5-10 월(月))중(中) 임간공동방목(林間共同放牧) 사업(事業)의 효과를 경제분석하면, 관행사육(慣行飼育)보다 293,075.,300원의 증체효과, 543,838,750원의 인건비(人件費) 절감효과 및 194,443,270원의 사료비(飼料費) 절감효과를 얻어 약(約) 1,031,357,320원의 소득효과를 가져왔다. 3) 208개(個) 공동방목장(共同放牧場)의 설문(設問) 조사(調査) 결과(結果), 농가(農家) 관행(慣行) 사육(飼育)보다 공동방목장(共同放牧場) 순위별(順位別) 효과에 대해서 농민들은 첫째 노동력(勞動力) 절감(節減). 둘째 사료비(飼料費) 절감(節減), 셋째 질병(疾病) 넷째 다두사육(多頭飼育) 가능(可能), 다섯째 협동심고취(協同心鼓吹), 여섯째 증체 효과, 일곱째 사양관리(飼養管理) 용역(容易), 여덟째 시설비(施設費) 절감(節減)을 들고 있다. 2. 공동방목(共同放牧) 사업(事業)의 문제점(問題點) 1) 임간공동방목(林間共同放牧) 2년차(年次)부터는 야생초(野生草)의 재생력(再生力)이 현저하게 저하(低下)되어 풀의 부족 현상이 일어난다. 2) 임간공동방목장(林間共同放牧場) 적지(適地)가 국유지(國有地)에 많으나 산림청(山林廳)의 이용(利用) 허가(許可)가 나지 않아 이용이 불가능하다. 3) 방목(放牧)으로 인(因)하여 발정(發精)한 암소를 발견하기 어려워서 수정시기(授精時期)를 놓치는 경우가 많다. 4) 각(各) 방목우(放牧牛)에 대한 방역(防疫) 및 진료(診療)의문제점이 많다. 3. 임간공동방목(林間共同放牧) 사업(事業)의 개선책(改善策) 1) 공동방목장(共同放牧場) 2년차(年次)부터는 겉뿌림초지(草地)나 제경초지(蹄耕草地)를 조성(造成)하여 충분한 조사료(粗飼料)를 확보(確保)시킬 것. 2) 정부(政府)는 강원도(江原道) 내(內) 모든 국유지(國有地)의 방목(放牧) 적지(適地)는 임간공동방목장(林間共同放牧場)으로 이용하여 우육(牛肉) 증산(增産), 독우(犢牛) 생산(生産) 지대(地帶)로 활용(活用)되도록 조치(措置)할 것. 3) 여지(與地)의 방목장(放牧場)에는 우수(優秀) 종빈우(種牝牛)를 혼목(混牧)시켜 번식성적(繁殖成績)을 올리도록 한 것. 그리고 발정(發情) 촉진(促進) 홀몬 주사(注射)로 동시(同時) 발정(發情)을 유도(誘導)해서 일괄 수정(授精)시킬 것. 4) 방목장(放牧場)에 토양병(土壤病)인 기종저의 예방(豫防) 주사(注射), 간질충에 대한 구충제의 년간(年間) 2회(回) 투여, 진드기 방제(防除)를 위하여 약욕(藥浴)을 시킬 것. 4. 임간공동방목장(林間共同放牧場) 육성(育成)을 위한 정책방향(政策方向) 1) 정부(政府)는 전국(全國)의 임야(林野)를 대상(對象)으로 임간공동방목장(林間共同放牧場) 적지(適地)를 조사(調査)할 것. 2) 정부(政府)는 임간공동방목장(林間共同放牧場) 적지(適地)로 판단되는 지역은 국공유림(國公有林)이나 법적(法的) 제한(制限) 지역(地域)도 목장(牧場) 개설(開設)이 가능하도록 조치할 것. 3) 정부(政府)는 여지(餘地)에 있는 공동방목장(共同放牧場) 적지(適地)에는 도로(道路) 개설(開設)과 전기목붕(電氣牧棚) 시설(施設)을 정부(政府) 자금(資金)으로 지원할 것. 4) 새마을 운동(運動)의 방향(方向)을 축산소득증대(畜産所得增大)에 두고 강원도(江原道)의 특성(特性)에 맞게 계속 임간공동방목(林間共同放牧) 사업(事業)이 추진(推進)될 수 있도록 정책적(政策的)인 배려가 필요하다. 5) 정부(政府)는 공동방목장(共同放牧場) 경영에 있어서 번식(繁殖) 성적(成績) 향상(向上)을 위한 인공수정상말비점(人工受精上末備点)을 보완(補完)해 줄 것. 6) 정부(政府)는 소 값의 적정(適定) 가격(價格) 수준(水準)을 유지(維持)하기 위한 가격(價格) 정책(政策)을 실시(實施)할 것. 7) 정부(政府)는 임간공동방목장(林間共同放牧場)에서 초지조성(草地造成)의 신청(申請)이 있을 때는 우선적으로 허가(許可)해 줄 것.
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.