Pattern recognition for evaluator errors in a credit scoring model for technology-based SMEs

S. Y. Sohn, M. K. Doo, Y. H. Ju

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

A credit scoring model for technology-based small and medium enterprises presupposes evaluator objectivity and evaluation consistency; however, there is always some amount of error in any technology evaluation. This can be due in part to the subjective evaluation attributes that comprise part of the credit scoring model. The evaluated values of subjective attributes can vary among evaluators. In this study, we identified the significant characteristics of both evaluator and evaluation teams in terms of evaluation error using a decision tree analysis. Our results can improve the accuracy of a wide range of evaluation procedures for technology financing.

Original languageEnglish
Pages (from-to)1051-1064
Number of pages14
JournalJournal of the Operational Research Society
Volume63
Issue number8
DOIs
Publication statusPublished - 2012 Aug

Bibliographical note

Funding Information:
Acknowledgements—This work (research) is financially supported by the Ministry of Knowledge Economy (MKE) and Korea Institute for Advancement in Technology (KIAT) through the Workforce Development Program in Strategic Technology. Man Jae Kim and Ji Won Kim have participated in the revision stage as graduate research assistant.

All Science Journal Classification (ASJC) codes

  • Management Information Systems
  • Strategy and Management
  • Management Science and Operations Research
  • Marketing

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