Abstract
This paper employs a cross-sectional research design to collect quantitative data for a group of Greek pharmaceutical companies in order to evaluate their credit risk. The data are processed using a variety of quantitative approaches, including series two-stage data envelopment analysis (DEA) combined with bootstrap and hierarchical clustering. The results of the two-stage DEA bootstrapped analysis indicate that the key problem with the firms’ performance is a lack of effectiveness rather than operating efficiency. The lack of a correlation between operating efficiency and effectiveness indicates that the firms’ performance metrics are unrelated. As a result, a bootstrapped DEA-based synthetic indicator is developed to be used with the other performance metrics as inputs to hierarchical clustering to divide sample firms into credit risk clusters. The series two-stage DEA bootstrapped approach used in this study could aid firms in evaluating their performance and increasing their competitive advantages.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
