본 연구의 목적은 가정과교육에서 창의 인성 교육에 대한 가정과 교사의 관심 단계와 실행 수준, 그리고 실행 실태를 알아보는데 있다. 연구 자료는 전국의 중학교 가정과 교사를 대상으로 체계적 표집과 편의 표집을 하여 우편과 이메일을 통해 설문지를 배포하고 회수된 187부를 최종 분석에 사용하였다. 조사 도구는 주로 Hall(1987)이 개발한 혁신에 대한 교사의 관심도와 실행 수준에 대한 질문지를 수정 보완하여 사용하였고 그 외는 선행연구를 기초하여 개발하였으며 타당도와 신뢰도를 검증하였다. 자료는 SPSS/window(12.0) 프로그램을 이용하여 빈도, 백분율, 평균, 표준편차, t-test, ANOVA를 실시하였다. 본 연구를 통해 밝혀진 결과를 요약하면 다음과 같다. 첫째, 창의 인성 교육에 대한 가정과 교사의 관심 단계는 정보적 관심 단계(85.51)가 가장 높았으며 다음으로 개인적 관심 단계(85.18), 운영적 관심 단계(81.88), 지각적 관심 단계(82.15), 강화적 관심 단계(68.80), 협동적 관심 단계(61.97), 그리고 결과적 관심단계(59.76)의 순으로 나타났다. 둘째, 창의 인성 교육에 대한 가정과 교사의 실행 수준은 기계적 실행 수준(수준 3; 21.4%)이 가장 많았으며, 다음으로 탐색 수준(수준 1; 20.9%), 정교화 수준(수준 5; 17.1%), 사용하지 않는 수준(수준 0; 15.0%), 준비 수준(수준 2; 10.2%), 통합 수준(수준 6; 5.9%), 갱신 수준(수준 7; 4.8%), 일상화 수준(수준 4; 4.8%) 순이었다. 셋째, 창의 인성 교육에 대한 가정과 교사의 실행 실태를 조사한 결과, 반 이상의 가정과 교사(56.1%)는 가정과 수업에서 인성 교육에 치중하고 있으며, 31.0%의 교사는 창의 인성 교육을 모두 실행한다고 응답하였다. 반면 소수의 교사(6.4%)는 창의성 교육을 실행한다고 응답하였고 같은 수의 교사(6.4%)는 창의성과 인성 교육 어느 것도 실행하지 않는다고 응답하였다. 가정과 교사의 창의 인성 교육 요소의 실행 정도를 조사한 결과, 창의 인성 교육 요소의 실행은 평균은 5점 만점에서 3.76이었고 창의성 요소의 평균은 3.59, 인성 요소의 평균은 3.94로 보통보다 높았다. 창의성 교육 요소의 실행 정도에 대해서, 개방성/민감성(3.97)을 가장 많이 실행하였고 다음으로 문제해결능력(3.79), 호기심/흥미(3.73), 비판적 사고(3.68), 논리/분석적 사고(3.63), 문제발견능력(3.61), 독창성(3.57), 유추성(3.47), 유창성/융통성(3.46), 정교성(3.46), 상상력(3.37), 몰입/공감(3.37)의 순으로 실행하였다. 인성 교육 요소는 실천력(4.07)을 가장 많이 실행하였고, 다음으로 협동/배려/공정(4.06), 자기관리능력(4.04), 시민의식(4.04), 진로개발능력(4.03), 환경친화능력(3.95), 책임(감)/소유(3.94), 의사결정능력(3.89), 신뢰/정직/약속(3.88), 자율성(3.86), 글로벌역량(3.55)의 순으로 실행한 것으로 나타났다. 창의 인성 교육을 실행할 때 어려운 점으로, 많은 가정과 교사(64.71%)는 창의 인성 교육을 실행할 수업 자료가 부족한 데 있다고 하였으며, 40.11%의 교사는 창의 인성 교육의 연수 기회가 적은데 있다고 응답하였다. 한편 38.50%의 가정과 교사는 창의 인성 교육에 대한 평가 기준을 설정하거나 평가 도구를 개발하는 것이 어렵다고 응답하였고, 25.67%의 교사는 창의 인성 교육 방법을 모른다고 응답하였다. 창의 인성 교육 실행을 위해서 필요한 지원에 대해서, '창의 인성 교육과 관련된 학생들의 체험활동의 확대'(4.34), '창의성과 인성을 중시하는 가정과 수업 문화 조성'(4.29), '학생 발달 단계에 적합한 창의 인성 교육 내용'(4.27), '창의 인성 교육을 담당할 교수 인력 확보'(4.21), '창의 인성 교육의 개념과 가치 확립'(4.09), '지역 사회 기업 등과 연계한 창의 인성 교육 추진'(3.94)의 순으로 응답하였다.
소비자의 구매 행위가 합리적이고 실용적인 방향으로 변화하는데 힘입어 할인점업계는 급속한 외형적인 성장과 함께 경쟁도 치열하다. 따라서 업계는 그 해결책으로 차별화와 수익성을 동시에 실현 시킬 수 있는 유통업체 브랜드(PB: Private Brand) 개발에 사활을 걸고 있다. 또한 치열한 경쟁 환경 하에서 생존하기 위해서는 고객만족을 넘어서 고객충성도를 높이는 것이 효과적인 방법임이 밝혀짐에 따라 PB가 고객충성도를 제고시키기 위한 전략적인 도구로 사용되고 있다. PB 이용 고객의 충성도를 높이려면 우선 고객집단의 특성을 파악해서 소비자가 지각하는 품질수준을 우선적으로 맞춰줘야 고객만족과 고객신뢰를 얻을 수 있고 결과적으로 고객충성도로 유도할 수 있다. 이에 본 연구는 지각된 품질에 영향을 미치는 선행요인과 고객충성도에 영향을 미치는 변수들 간의 관계에 대한 체계적인 분석결과를 제시하기 위해 선행연구에서 검증된 인과관계를 기반으로 연구모형과 연구가설을 설정했고, 주요 연구결과는 다음과 같다. 기업명성, 브랜드명성, 제품경험, 브랜드친숙도가 높을수록 지각된 품질이 높아지고, 지각된 품질이 높을수록 고객만족, 고객신뢰, 고객충성도가 높아지며, 고객만족과 고객신뢰가 높을수록 고객충성도가 높아지는 것으로 조사되었다. 또한 기업명성이 지각된 품질에 미치는 영향력은 PB가 NB보다 높게 나타난 반면 브랜드명성과 브랜드친숙도가 지각된 품질에 미치는 영향력은 NB가 PB보다 높게 나타났다. 이러한 실증분석 결과는 지각된 품질에 영향을 미치는 선행요인과 결과요인에 대한 보다 명확한 이해를 바탕으로 실무자가 마케팅 활동을 하는데 유용하게 활용할 수 있을 것이다.
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.