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<p>This study addresses the crucial issue of evaluating university performance by developing a model that assesses how effectively Indian universities at the course level convert resources into student career success, specifically in terms of job placements and pathways to higher education. The goal is to develop a framework that considers stakeholder priorities and captures the efficiency assessment beyond traditional rankings. We propose an advanced hybrid model that combines Analytic Hierarchy Process (AHP) with super-efficiency (SE) Data Envelopment Analysis (DEA) based on the Directional Distance Function (DDF). This new model mitigates a number of limitations associated with traditional methods by fully integrating the judgments of experts as stakeholders into the efficiency calculations, and enables rankings of all the institutions while searching for the top-rated institutions. The model was tested on B.Tech programs of 28 Indian universities using National Institute for Ranking Framework (NIRF) 2020-21 data. This revealed significant performance disparities, with efficiency scores ranging from 0.8269 to 1.5932. The analysis identified three institutions (U1, U4, and U20) as exceptionally effective benchmarks superior at converting resources into valuable student outcomes. This framework establishes a valuable tool for internal benchmarking, strategic planning, and efficient resource allocations for university administrators, while developing measures that are outcome-based for students and policymakers beyond reputation-based rankings.</p>

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An Integrated AHP-DEA Model for Evaluating Indian Universities’ Performance in Placement and Post-Graduate Pursuit

  • 期刊论文

  • 发表日期：

    2026年3月17日

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  • Shivani Kalyan,
  • Pooja Bansal

摘要

This study addresses the crucial issue of evaluating university performance by developing a model that assesses how effectively Indian universities at the course level convert resources into student career success, specifically in terms of job placements and pathways to higher education. The goal is to develop a framework that considers stakeholder priorities and captures the efficiency assessment beyond traditional rankings. We propose an advanced hybrid model that combines Analytic Hierarchy Process (AHP) with super-efficiency (SE) Data Envelopment Analysis (DEA) based on the Directional Distance Function (DDF). This new model mitigates a number of limitations associated with traditional methods by fully integrating the judgments of experts as stakeholders into the efficiency calculations, and enables rankings of all the institutions while searching for the top-rated institutions. The model was tested on B.Tech programs of 28 Indian universities using National Institute for Ranking Framework (NIRF) 2020-21 data. This revealed significant performance disparities, with efficiency scores ranging from 0.8269 to 1.5932. The analysis identified three institutions (U1, U4, and U20) as exceptionally effective benchmarks superior at converting resources into valuable student outcomes. This framework establishes a valuable tool for internal benchmarking, strategic planning, and efficient resource allocations for university administrators, while developing measures that are outcome-based for students and policymakers beyond reputation-based rankings.