(untitled) (gjstudies, 2014)
This research investigates the novel application of computational network analysis andsemantic modeling techniques to evaluate and enhance Integrated Reporting Frameworks(IRFs) for long-term stakeholder value creation. Departing from traditional accounting andqualitative assessment methods, we propose a hybrid methodology that combines principles from complex systems theory, natural language processing, and multi-agent simulationto model the dynamic, non-linear relationships between corporate disclosures, stakeholderperceptions, and long-term value outcomes. The core innovation lies in conceptualizingan IRF not as a static document but as a dynamic information ecosystem. We develop acomputational model that simulates how different structural and semantic properties of anintegrated report—such as connectivity density between financial and non-financial capitals,narrative coherence, and temporal linkage strength—propagate through a network of heterogeneous stakeholders (investors, employees, communities, regulators). Our results, derivedfrom both simulated data and a curated corpus of early adopter reports from 2000-2004, reveal previously unexamined leverage points. We identify a non-linear threshold effect wherethe value-creation impact of connectivity within a report accelerates significantly only afterachieving a minimum semantic integration score. Furthermore, the simulation demonstratesthat optimizing for a narrow set of powerful stakeholders can create systemic fragility, reducing long-term resilience. The model predicts that frameworks emphasizing bidirectionalfeedback loops and temporal narratives outperform those focused on contemporaneous snapshot integration. This research contributes a new, computationally-grounded paradigm fordesigning and evaluating reporting frameworks, moving beyond compliance to engineer themas tools for systemic value creation. The findings suggest that the next evolution of IRFsrequires embedded computational tools to manage complexity and stakeholder interaction