PhD Oral Exam - Sarah Farahdel, Information Systems Engineering

When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.

Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.

Abstract

In response to the urgent need for organizations to comprehensively assess their sustainability performance, this research introduces a novel approach through the development of the Systematic Comprehensive Integrative Indicator-based Sustainability Assessment Framework (SCII-SAF). This framework addresses the limitations of existing sustainability assessment frameworks (SAFs) by providing a systematic and integrative method for selecting, evaluating, and understanding sustainability indicators (SIs). A thorough literature review of SAFs across diverse industries reveals a critical gap: the lack of a universal method for identifying the most relevant indicators and assessing their interdependencies without heavy reliance on subjective expert judgment. Current multi-criteria decision-making models, offer valuable insights but suffer from their dependence on expert opinion, leading to subjective assignments of importance. To address this, this research proposes an innovative data-driven approach that integrates correlation analysis and network analysis for evaluating static relationships and interdependencies, and system dynamics simulation to capture dynamic changes and the performance impact of SIs over time. This hybrid methodology, combining graph theory with machine learning, represents a unique approach not previously applied in academic literature. By minimizing reliance on expert judgment, the SCII-SAF enhances objectivity and analytical rigor. The SCII-SAF model provides a groundbreaking, data-driven framework that aligns SIs with long-term sustainability objectives, offering decision-makers valuable insights. It facilitates informed decision-making by quantifying trade-offs between economic, social, and environmental dimensions of sustainability, tailored to specific industry needs. By integrating Network Analysis for structural understanding and System Dynamics Simulation for temporal and dynamic insights, this research not only addresses significant limitations in existing SAFs but also represents a transformative contribution to sustainability assessment practices.