Date of Award

Spring 6-12-2026

Document Type

Dissertation

Degree Name

Ph.D. in Business

Organizational Unit

Daniels College of Business

First Advisor

Daniel Baack

Second Advisor

Jack Strauss

Third Advisor

Conrad Ciccotello

Copyright Statement / License for Reuse

All Rights Reserved
All Rights Reserved.

Keywords

Dynamic capabilities, Artificial intelligence, Business analytics, Text analysis, Enterprise reporting, Industry dynamics, Resource-based view (RBV), Corporate disclosures, Retrieval-augmented generation

Abstract

GraphSTRAT (Graph-Structured Relational Analytics for Theory), a Graph-based Retrieval-Augmented Generation (GraphRAG)-based Large Language Model (LLM) method, leverages domain-specific ontology extraction to build context-aware measures for text-based business research, creating: a novel, integrated Dynamic Capabilities Index (DCI); an artificial intelligence (AI) deployment index (AIDI); and a Dynamic Capabilities Knowledge Graph Explorer for a corpus of corporate disclosure filings. The first paper demonstrates a new method for extracting actor–action–object relationships from 10-K filings by operationalizing Dynamic Capabilities (DC) theory and shows that the resulting Dynamic Capabilities Knowledge Graph Explorer provides provenance and visibility, allowing researchers to inspect and trace claims to exact passages. The second paper simultaneously evaluates DC and the maturity of AI deployment, creating DCI and AIDI from quantifiable data to link DC and AI to a firm’s performance. The results indicate that AIDI predicts near-term profitability pressures and increases in R&D intensity, and that DCI predicts market-based outcomes and strategic reconfiguration proxies. A directional predictive relationship from AIDI to later DCI is also observed, consistent with AI implementation inducing governance, routines, and reconfiguration capabilities. The results demonstrate how relational, auditable measurement improves theory alignment and enables new tests of technology-capability complementarities in large-scale archival settings. The research extends text-as-data methods from word-level themes to theory-anchored relational structures; introduces auditable LLM-based construct measurement that enables relational-level validation; and provides new empirical evidence and insights into large firms’ Dynamic Capabilities and AI deployment maturity.

Copyright Date

6-2026

Publication Statement

Copyright is held by the author. User is responsible for all copyright compliance.

Rights Holder

Gordon Broadbent IV

Provenance

Received from ProQuest

File Format

application/pdf

Language

English (eng)

Extent

116 pgs

File Size

2.3 MB

Download

Available for download on Sunday, July 09, 2028



Share

COinS