Information Usage as a Performance Driver in Supply Chains: Integrating Machine Learning, Structural Modeling, and Efficiency Analysis

Author

• Asli Z. DagFollow

Completion Date

Spring 5-13-2026

Document Type

Thesis

Degree Name

Doctor of Philosophy (PhD)

Program or Discipline Name

Data Sciences

First Advisor

Dr. Kayden Jordan

Abstract

The present study investigates ways in which companies utilize information to improve suppliers’ performance within their supply chains. In the literature, the existing studies focus largely on information sharing between companies and suppliers, while less is known about how organizations then apply shared information during decision-making in ways that impact outcomes for suppliers. Importantly, the extant research demonstrates that information sharing can improve the visibility of issues arising along the supply chain, raise the coordination between those within the chain, and reduce uncertainty about the necessary supply and demand; however, the benefits in these areas remain limited unless the relevant information is employed effectively.

In this context, the value of information becomes clear only when it is integrated into coordination and planning. Therefore, information usage is the vital link between the initial information sharing and subsequent gains in suppliers’ performance. To bridge the gap that often exists in place of that essential connection, this research combines three complementary methods: a Genetic Algorithm to recognize critical predictors, Structural Equation Modeling to assess causal pathways, and Data Envelopment Analysis to evaluate the efficiency of suppliers.

This study’s empirical analysis employs data from 269 suppliers in the U.S. aerospace industry, based on which we measure constructs including information quality, information usage, normative commitment, and instrumental commitment. For its theoretical foundation, this study draws on Organizational Information Processing Theory, the Resource-Based View, and Commitment-Trust Theory.

The results show that information usage has a significant positive effect on supplier performance and mediates the effects of information sharing and information quality. While information sharing and information quality contribute to performance, their effects are primarily realized through information usage rather than direct influence. In addition, efficiency analysis reveals that suppliers differ substantially in how effectively they transform informational and relational capabilities into performance outcomes, with information usage emerging as the most critical driver of efficiency. These findings indicate that performance improvements depend not only on the availability or quality of information but on how effectively that information is used in decision-making processes.

Recommended Citation

Dag, A. Z. (2026). Information Usage as a Performance Driver in Supply Chains: Integrating Machine Learning, Structural Modeling, and Efficiency Analysis. Retrieved from https://digitalcommons.harrisburgu.edu/dandt/99