• The Journal of the Korea Contents Association
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• v.8 no.9
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• pp.234-241
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• 2008

The feature of the hotel business as a labor intensive industry and its heavy dependence on man power is relatively bigger than other industries. the important factors influencing the customer`s decision making are tangible facilities and intangible service qualities. however, the changes in economic situation are also seriously influencing them. So hotels are started to find other IT(information technology) systems. BSC which has been recognized as one of barometers to establish management performance is one of them. The purpose of this study was to develop KPI(key performance indicator) by using the BSC(Balanced Scorecard) for evaluating hotel management performance. This thesis presents customer performance, inner process performance, learning and growing performance as non-financial factors and tries to examine the cause and effect in the hotel industry. Hotels have to know nonfinancial performance which has positively relate to financial performance. To introduce BSC system is not to lead increasing income and bettermenting service quality, satisfacting customer needa for hotels, But to lead developing value enhancement to hotel enterprises and present process.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1293-1305
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• 2016

In this paper, we develop debt collection predictive models for the person in arrears by utilizing the direct loan data of the Korea Student Aid Foundation. We suggest credit risk scorecards for overdue student direct loan using the developed 3 models. Model 1 is designed for 1 month overdue, Model 2 is designed for 2 months overdue, and Model 3 is designed for overdue over 2 months. Model 1 shows that the major influencing factors for the delinquency are overdue account, due data for payment, balance, household income. Model 2 shows that the major influencing factors for delinquency loan are days in arrears, balance, due date for payment, arrears. Model 3 shows that the major influencing factors for delinquency are the number of overdue in recent 3 months, due data for payment, overdue account, arrears. The debt collection predictive models and credit risk scorecards in this study will be the basis for segmented management service and the call & collection strategies for preventing delinquency.