This paper presents an Elliptic Intuitionistic Fuzzy Quad (E-IFQ) extension of our prior Intuitionistic Fuzzy (IF) productivity model and its circular IF variant for evaluating habilitated academic staff in STEM higher education. Productivity is assessed across core dimensions (research output, teaching/mentoring load, project leadership, academic service). Unlike the IF and circular IF models, the E-IFQ aggregation yields a centroid (aggregated membership and non-membership degrees) that can differ due to the asymmetric representation of uncertainty. The novelty lies in substituting the symmetric (circular) hesitation zone with an elliptical one, parameterized by a major and a minor axis. These axes capture asymmetric dispersion in expert assessments, allowing disagreement to manifest differently along the membership and non-membership directions. This representation separates the central value (consensus) from directional uncertainty (asymmetry), enriching interpretability and highlighting the nature of evaluator disagreement. A numerical case study illustrates that, under uneven evidence across criteria, the E-IFQ model yields more informative evaluation profiles than the circular IF counterpart, pinpointing whether disagreement concentrates in positive or negative assessments, while also capturing changes in the central score. This geometric refinement supports transparent reporting and nuanced, uncertainty-aware promotion and planning decisions at institutional level.

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An Elliptic Intuitionistic Fuzzy Model for Evaluating Habilitated Academic Staff Productivity in Higher Education

  • Velichka Traneva,
  • Cengiz Kahraman,
  • Stoyan Tranev,
  • Venelin Todorov

摘要

This paper presents an Elliptic Intuitionistic Fuzzy Quad (E-IFQ) extension of our prior Intuitionistic Fuzzy (IF) productivity model and its circular IF variant for evaluating habilitated academic staff in STEM higher education. Productivity is assessed across core dimensions (research output, teaching/mentoring load, project leadership, academic service). Unlike the IF and circular IF models, the E-IFQ aggregation yields a centroid (aggregated membership and non-membership degrees) that can differ due to the asymmetric representation of uncertainty. The novelty lies in substituting the symmetric (circular) hesitation zone with an elliptical one, parameterized by a major and a minor axis. These axes capture asymmetric dispersion in expert assessments, allowing disagreement to manifest differently along the membership and non-membership directions. This representation separates the central value (consensus) from directional uncertainty (asymmetry), enriching interpretability and highlighting the nature of evaluator disagreement. A numerical case study illustrates that, under uneven evidence across criteria, the E-IFQ model yields more informative evaluation profiles than the circular IF counterpart, pinpointing whether disagreement concentrates in positive or negative assessments, while also capturing changes in the central score. This geometric refinement supports transparent reporting and nuanced, uncertainty-aware promotion and planning decisions at institutional level.