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Using Structural Variation Analysis to Measure Interdisciplinary Knowledge Integration in Operations Research

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  • Khaled Mili

    (Department of Quantitative Methods, College of Business Administration, King Faisal University, Saudi Arabia)

  • Majdi Argoubi

    (University of Sousse, Tunisia)

Abstract

Traditional citation analyses often fail to capture how research reshapes intellectual landscapes. This study applies Structural Variation Analysis (SVA) to assess the interdisciplinary impact of a highly cited paper on the Cross-Entropy Method in Operations Research. Using co-citation network analysis, the authors examine structural shifts through key metrics, including modularity change (∆M = 52.57), cluster linkage (CL = 135.2), centrality divergence (CKL = 0.47), and entropy (E = 0.98). The findings reveal that this methodological paper plays a pivotal role in bridging previously unconnected research domains. Beyond accumulating citations, the Cross-Entropy Method has fundamentally altered research connectivity. SVA offers an early indicator of transformative research before conventional citation metrics can capture their full impact.

Suggested Citation

  • Khaled Mili & Majdi Argoubi, 2025. "Using Structural Variation Analysis to Measure Interdisciplinary Knowledge Integration in Operations Research," International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), IGI Global, vol. 16(1), pages 1-16, January.
    • Handle: RePEc:igg:jssmet:v:16:y:2025:i:1:p:1-16
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