{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T15:06:18Z","timestamp":1719414378254},"reference-count":33,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2010,10,22]],"date-time":"2010-10-22T00:00:00Z","timestamp":1287705600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Quality & Reliability Eng"],"published-print":{"date-parts":[[2010,12]]},"abstract":"Abstract<\/jats:title>The focus of the paper is the use of optimal scaling techniques to reduce the dimensionality of ordinal variables describing the quality of services to a continuous score interpretable as a measure of operational risk. This new score of operational risk is merged with a financial risk score in order to obtain an integrated measure of risk. The proposed integration methodology is a generalization of the merging model suggested in Fagini and Giudici (J. Oper. Res. Soc<\/jats:italic>. 2010; in press) for a hierarchical data structure. In order to demonstrate the methodology, we use real data from a telecommunication company providing services to enterprises in different business lines and geographical locations. For each enterprise, we have collected information about operational and financial performance. The approach demonstrated in this case study can be generalized to general service providers who are concerned by both the quality of service and the financial solvency of their customers. 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