정부는 보건진료원으로 하여금 지역주민들에게 보다 더 의욕적으로 양질의 보건의료서비스를 제공하도록 하기 위하여 1992년 4월 1일부터 보건진료원을 별정직 공무원으로 정규직화 하였다. 본 연구는 보건진료원의 정규직화가 보건진료원의 업무활동과 보건진료소의 관리운영체계에 미친 영향을 분석하기 위해 경상남도와 경상북도의 보건진료소 중 집락추출법과 단순확률추출법으로 50개소를 뽑아 보건진료원을 대상으로 직접 면담조사하고 제반기록 및 보고서에서 필요한 자료를 발췌하였다. 조사기간은 1992년 1월 1일에서 3월 31일까지(정규직화 이전)와 1993년 1월 1일에서 3월 31일까지(정규직화 이후)였다. 보건진료원들의 96%가 정규직화를 원했는데 그 이유는 신분보장과 보수가 좋아지리라는 것이었다. 정규직화 후 보건진료원직을 자랑스럽게 생각한다는 사람이 24%에서 46%로 증가하였다. 신분보장에 대해서는 항상 불안하다는 사람이 30%에서 10%로 감소하였다. 정규직화 후 월평균 급여액은 802,600원에서 1,076,000원으로 34% 증가했으며 90%가 만족한다고 했다. 업무 내용별 자율성 인지정도는 업무계획, 업무수행, 진료소관리(재정)운영, 업무평가 영역에 대한 자율성 인지도가 정규직화 후에 증가되었다. 보건진료원의 활동내용 중 지역사회 자원파악, 지도작성상태, 지역사회조직 활용정도, 인구구조 파악정도와 가정건강기록부 작성은 정규직화 후에 특별한 변화는 없었다. 또한 집단보건교육, 개인보건교육, 학교보건교육의 실시도 정규직화 후에 변화가 없었다. 그러나 가정방문 실시현황은 1인당 월평균 13.6%회에서 정규직화 후에는 27.5%회로 늘었다. 모성보건 및 가족계획 사업 그리고 예방접종도 정규직화 후에 타기관에 의뢰하는 것이 더 늘었다. 통상질병관리 가운데 성인병관리는 3개월 동안 1개 진료소당 평균 고혈압환자는 12.7%명에서 11.6명으로, 암환자는 1.5명에서 1.2명으로, 당뇨병환자가 4.3명에서 3.4명으로 줄었다. 각종 기록부 비치상황은 장비대장, 약품관리 대장, 환자진료기록부는 100% 비치되었으나 기타 기록부는 그렇지 않았고 정규직화 후에도 변화는 없었다. 보건진료소가 보건소로부터 지원을 받는 내용은 약품 14.0%에서 30%로, 소모품 22.0%에서 52.0%로, 건물유지 및 보수가 54.0%에서 68% 로, 보건교육 자료가 34.0%에서 44.0%로 증가하였고, 장비는 58.0%에서 54.0%로 감소했다. 보건진료소의 월평균 수입은 진료수입이 약 22,000원 증가했고, 국비 또는 지방비 보조금이 4,800원에서 38,508%원으로 증가했으나 회비 및 기부금은 줄어 총수입은 약 50,000원 증가했다. 지출총액은 큰 변동이 없었다. 보건소로부터 3개월 동안 받은 지도감독 중 지시공문을 받은 진료소가 20%에서 38%로 늘었고, 방문지도는 79%에서 62%, 회의소집은 88%에서 74%로 감소하였다. 전화지도는 보건진료소당 평균 1.8회에서 2.1회로 늘었다(p<0.01). 면보건요원과의 협력관계가 있다고 한 보건진료원은 42%에서 36%로 감소하였다. 보건소장과의 관계가 좋다는 보건진료원이 46%에서 24%로 감소하였고, 보건행정계장과 관계가 좋다는 사림이 56%에서 36%로 감소하였다(p<0.05). 보건진료소 운영협의회 회장과의 관계가 좋다는 사람은 62%에서 38%로 감소되었고 보건진료소 운영협의회가 보건진료소에 별로 도움이 안된다와 전혀 도움이 되지 않는다는 사람이 정규직화 전과 후에 각각 92.0%, 82.0%였다. 운영협의회가 필요 없다는 사람은 정규직화 전에 4%에서 16%로 증가되었다(p<0.05). 보건진료원제도 발전을 위해 제안된 사항은 보건교육중심의 활동, 보건진료소운영의 자율성 보장 보건소에 경험이 풍부한 보건진료원을 두어 지도감독하게 할 것과 사용하는 약품의 종류를 늘려 줄 것 등이었다. 이상의 결과로 보하 정규직화 후 보건진료원의 역할, 기능 등의 업무활동의 변화는 거의 없었으나 신분보장과 봉급에 대한 만족도는 향상이 되었고 또한 자율성도 증가하였다. 보건소의 지원은 약간 늘었으며, 지도감독체제에서 지시 공문의 증가로 사무보고 업무가 많아지고, 근무 확인을 위한 전화감독은 늘었으나 업무치진을 위한 행정직 지도 또한 기술적 지도는 거의 없었다.
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.
본 연구에서는 항공권 예약 발권 웹사이트의 서비스 품질을 측정 뿐만 아니라 서비스 회복도 측정하고자 하였다. 또한 서비스 품질과 서비스 회복이 고객만족 및 충성의도에 미치는 영향관계를 실증하고자 하였다. 온라인 서비스 품질과 온라인 서비스 회복의 측정을 위해 Parasuraman, Zeithaml, & Malhotra(2005)가 개발한 E-S-QUAL과 E-RecS-QUAL을 사용했으며, 했다. E-S-QUAL은 온라인 서비스 품질을 측정하는 도구로써, 효율성, 시스템 이용가능성, 이행성, 프라이버시의 4개 차원 22개 항목으로 구성된다. E-RecS-QUAL은 온라인 서비스 회복을 측정하는 도구로써, 반응, 보상, 접촉의 3개 차원 11개 항목으로 구성된다. 실증분석을 위한 설문조사는 항공사나 여행사의 웹사이트를 통해 국내 외 항공권을 구입해 본 경험이 있는 소비자를 대상으로 실시하였는데, 총 400부가 회수되었고, 이 중 342부를 최종분석에 사용하였다. 실증분석을 위해 AMOS 7.0과 SPSS 15.0을 사용하였다. 먼저, SPSS 15.0을 사용하여, 요인점수를 이용한 회귀분석으로 가설검증을 한 결과, <가설 I-1, 2, 3, 4, II-1, 2, 3, III-1, IV-1>이 전부 채택되었다. 온라인 서비스 품질과 온라인 서비스 회복의 각 차원은 모두 전반적인 서비스 품질에 유의한 영향을 보였고, 전반적인 서비스 품질은 고객만족에 유의한 영향을 미쳤다. 마지막으로 고객만족 역시 충성의도에 유의한 영향을 미치는 것으로 확인되었다. 한편 AMOS 7.0을 사용하여 모형 분석을 하였는데, 모형의 적합도는 가설검증을 하기에 합당한 수치가 나왔다. 이를 토대로 가설검증을 한 결과, <가설 I-1, 3, II-1, 3, III-1, IV-1>은 채택되었고, <가설 I-2, 4, II-2>는 기각되었다. 이 결과는 Parasuraman et al.(2005)이 주장한 것처럼 E-S-QUAL을 나타내는 데는 요인점수를 이용한 회귀분석이 더 적합하다는 것을 보여주는 것이라고 판단된다. 이를 토대로 본 연구의 시사점을 정리하였다.