본 연구는 근거이론 관점에서 학점은행제 평가체제의 개선요구 요인을 피 평가기관 담당자의 심층면접을 통한 연구결과를 토대로 교육적․정책적 시사점을 제시하기 위한 것이다. 이를 위하여 본 연구는 학점은행제 피 평가기관 담당자 26명을 연구 참여자로 최종적으로 선정한 후, 심층면담을 통하여 자료를 수집하였다. 이는 Glaser와 Strauss(1967)가 제시한 근거이론 방식을 적용하여 개방코딩-축코딩-선택코딩의 순으로 분석절차를 진행하였다. 본 연구의 분석결과, 중심현상은 기본운영 여건, 질 관리 점검, 역량(성과)평가 영역의 구체화에 대한 변화였다. 이를 근간으로 인과적 조건, 맥락적 조건, 중재적 조건 순으로 학점은행제 평가체제의 개선요구 요인에 대한 패러다임 모형을 제시하였다. 이상의 결과를 토대로 신규평가, 재평가의 이원화된 평가지표 개선, 교․강사의 경력 인정 지표 개선, 수업목표와 관련한 평가지표 개선, 원격기관의 ‘조교 역할’ 지표 재구성 측면에서 정책적 함의를 제고시킬 수 있는 방안을 제시하였다.

기술금융은 금융당국이 금융산업 선진화, 중소기업발전을 위해 강력한 정책적 의지를 가지고 도입하여 시행하고 있는 분야이다. 이러한 배경으로 은행의 자체적인 기술평 가가 2016년 9월부터 시행되었다. 기술우수기업은 기술평가과정에서 산출된 높은 기술등급으로 기존 신용등급이 상향되게 되며, 결과적으로 높아진 신용등급만큼 금융거래시 혜택을 받게 된다. 분석대상은 KEB하나은행이 2016년 9월부터 2017년 하반기까지 수행한 기술평가 대상 2,719개 업체를 분석하였다. 2016년 하반기 수행된 406개 업체에 대한 기술력평가 예비 연구에서 기존 신용등급과 산출된 기술등급을 결합한 결과, J58‘출판업‘의 기술신용등급은 신용등급대비 1.05등급 상향되어 상향정도가 가장 높았으며, C10‘식료품 제조업‘이 두 번째로 상향정도가 높았다. 이로써 기술력평가를 통한 수혜업종을 가려낼 수 있었으며, 업종별 기술 평가의 유용성을 확인할 수 있었다. 이에, 전체 수행기간동안 평가된 2,719개 업체에 대하여 기술력, 업력, 성장유망업종별 분석을 수행하였다. 분석결과 기술력 T-4이상 등급 업체들의 신용등급 상향정도가 가장 높았으며 5년 기준 업력에 따른 기술력평가의 유효성은 미미한 것으로 파악되었다. 정책지원의 효율성차원에서 지정된 성장유망업종에 해당하는 업체들을 대상으로 분석한 결과 일반기업대비 신용등급의 상향정도가 높아 성장유망업종 지원의 유용성을 확인할 수 있었다. 향후, 은행의 업체 발굴 또는 당국의 정책수립시에 T-4이상의 기술력 우수기업, 성장유망 업종에 집중하면 자금지원 효과를 극대화할 수 있을 것으로 판단된다.

This study investigated the difference of the effects of public loan programs in fishery industry on management performance from a balanced score card (BSC) perspective depending on the type of loan, scale of fund, period of support and business category, using the financial data of fisheries firms having the balance of loan at the end of 2014. The key factors influencing credit rating change were also analyzed after public loan support. From a integrative perspective, results show that the firms supported by working fund have higher management performance than the firms supported by facility fund. The firms received large scale fund showed higher management performance than the firms received small scale fund. While management performance was decreasing or slowing down over time after financial support, management performance of the firms supported by facility fund improved over time. From a non-financial perspective, the firms received facility fund invested more in education and growing perspective than the firms received working fund. As the size of fund increased, the investment in education, growing, internal process and customer increased. Personnel expenses and employee benefits for education and growing has increased over time. However, the firms with facility fund restricted the expenses of education, personnel expenses and employee benefits as time goes by. Because the effects of public loan on credit rating of fisheries corporations have no statistical significance, it has become known that the financial support of public loan program has no influence on the change of credit rating of fisheries corporations. This study attempted performance analysis from a BSC perspective which combine factors of nonfinancial perspective with factors of financial perspective. Findings from this study suggest the direction of microscopic performance analysis of public loan in fishery industry.

Most researches on the corporate credit rating are generally classified into the area of bankruptcy prediction and bond rating. The studies on bankruptcy prediction have focused on improving the performance in binary classification problem, since the criterion variable is categorical, bankrupt or non-bankrupt. The other studies on bond rating have predicted the credit ratings, which was already evaluated by bond rating experts. The financial institute, however, should perform effective loan evaluation and risk management by employing the corporate credit rating model, which is able to determine the credit of corporations. Therefore, in this study we present a medium sized corporate credit rating system by using Artificial Neural Network(ANN) and Analytical Hierarchy Process(AHP). Also, we developed AHP model for credit rating using non-financial information. For the purpose of completed credit rating model, we integrated the ANN and AHP model using both financial information and non-financial information. Finally, the credit ratings of each firm are assigned by the proposed method.

The paper investigates the mechanism through which corporate credit ratings affect dividend payments by decomposing the mean difference of dividends into a part that is explained by the determinants of dividends and a residual part that is contributed by the pure credit group effect, in the framework of the traditional dividend model of Fama and French (2001). Historically, better credit rated firms have shown consistently higher propensity to pay dividends especially during the economic crisis period. According to the counter-factual decomposition technique of Jann (2008), better rated firms are more responsive to the firm characteristics that have positive impact on dividends and poor rated firms are more responsive to the negative dividend predictors. As a result, good (bad) credit ratings make corporate managers become more bold (timid) in their dividend payments and they tend to pay more (less) dividends than what their firm characteristics prescribe. The degree of information asymmetry increases for the poor group firms during crisis periods and they attempt to reserve more cash in preparation for future investments. The decomposition results suggest that the credit group effect can potentially exceed the effect of firm characteristics because firms of different credit ratings can respond to the very same firm characteristics in a different manner.

In the Korean capital market, there are three credit rating agencies. Potential credit ratings based on credibility in the financial market are calculated independently for each rating agency. It often happens that despite the fact that the grades of the rating agencies are the same and have the same rating system, their actual ratings are different, even for the same firm. In such circumstances, investors may wonder why. In this study, we assume that the cause is the information environment in which the company operates. The credit ratings of rating agencies are mainly classified into bonds or commercial papers. The bonds are rated primarily for long-term of three years or more, and commercial papers specify ratings for less than one year. The information environment to be verified in this study was observed with a commercial paper. Under the assumption the larger the analyst following is, the more transparent is the information environment, we analyzed the influence of the number of analysts following on the degree to which ratings conflicted among credit rating agencies. The results of our analysis confirmed that opinion conflict among credit rating agencies is clearly reduced for companies with good information environments.

Purpose - The purpose of this research is to make a comparative assessment of People Credit Funds (PCFs) ranking in Vietnam between the Circular No. 42/2016/TT-NHNN dated December 20, 2016 with the Decision No. 14/2007/QD-NHNN dated 09/4/2007 issued by the Governor of the State Bank. Research design, data, and methodology - This study is mainly based on the Circular No. 42/2016/TT-NHNN dated December 20, 2016 and the Decision No. 14/2007/QD-NHNN dated 09/4/2007 issued by the Governor of the State Bank on PCFs ranking. Results - The study paper has shown positive changes in PCFs ranking in Vietnam in accordance with the Circular No. 42/2016/TT-NHNN, such as increasing Capital Adequacy Ratio (CAR), maintaining CAR, improving assets quality, developing indicators of governance, management and control capability. These changes have implications for the development and efficient performance of PCFs in Vietnam. Conclusions - The classification and evaluation of PCFs will contribute to its healthy development. These finding support PCFs to understand more about rating methodology, significance of rating system and the importance of improving their rating. PCFs in Vietnam desire to develop their business effectively, they need to understand exactly and comply fully with regulations related to their field of operations.