Loan consultants assist clients with loan application processing and loan decisions. Their duties may include contacting people to ask if they want a loan, meeting with loan applicants and explaining different loan options. We studied the efficiency of service quality of loan consultants contracted to a bank in Korea. They do not work as a team, but do work independently. Since he/she is not an employee of the bank, the consultant is paid solely in proportion to how much he/she sell loans. In this study, a consultant is considered as a decision making unit (DMU) in the DEA (Data Envelopment Analysis) model. We use a principal component analysis-data envelopment analysis (PCA-DEA) model integrated with Shannon’s Entropy to evaluate quality efficiency of the consultants. We adopt a three-stage process to calculate the efficiency of service quality of the consultants. In the first stage, we use PCA to obtain 6 synthetic indicators, including 4 input indicators and 2 output indicators, from survey results in which questionnaire items are constructed on the basis of SERVQUAL model. In the second stage, 3 DEA models allowing negative values are used to calculate the relative efficiency of each DMU. In the third stage, the weight of each result is calculated on the basis of Shannon’s Entropy theory, and then we generate a comprehensive efficiency score using it. An example illustrates the proposed process of evaluating the relative quality efficiency of the loan consultants and how to use the efficiency to improve the service quality of the consultants.

Entropy is a measure of disorder or uncertainty. This terminology is qualitatively used in the understanding of its correlation to pollution in the environmental area. In this research, three different entropies were defined and characterized in order to quantify the qualitative entropy previously used in the environmental science. We are dealing with newly defined distinct entropies E1, E2, and E3 originated from Shannon entropy in the information theory, reflecting concentration of three major green house gases CO2, N2O and CH4 represented as the probability variables. First, E1 is to evaluate the total amount of entropy from concentration difference of each green house gas with respect to three periods, due to industrial revolution, post-industrial revolution, and information revolution, respectively. Next, E2 is to evaluate the entropy reflecting the increasing of the logarithm base along with the accumulated time unit. Lastly, E3 is to evaluate the entropy with a fixed logarithm base by 2 depending on the time. Analytical results are as follows. E1 shows the degree of prediction reliability with respect to variation of green house gases. As E1 increased, the concentration variation becomes stabilized, so that it follows from linear correlation. E2 is a valid indicator for the mutual comparison of those green house gases. Although E3 locally varies within specific periods, it eventually follows a logarithmic curve like a similar pattern observed in thermodynamic entropy.