Date of Award

8-2025

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Degree Discipline

Business Administration

Abstract

As construction transitions into an era shaped by Industry 4.0 and data-driven decision-making, Artificial Intelligence (AI) emerges as a transformative tool with the potential to enhance key performance indicators (KPIs) such as schedule adherence, cost management, quality assurance, safety, resource allocation, and customer satisfaction. However, few studies have empirically assessed the direct influence of AI adoption on operational performance or examined the innovation diffusion factors that facilitate its integration. This gap underscores the need to align AI initiatives with broader organizational strategies, ensuring that technological adoption translates into measurable improvements in construction performance.

This quantitative study investigated two core research questions: (1) How does the level of AI adoption impact performance in construction? (2) How do diffusion-related factors, relative advantage, compatibility, complexity, trialability, and observability, influence AI adoption in construction firms? Data was collected using a structured survey of professionals across various construction industry sectors. The study assessed both measurement and structural models using Partial Least Squares Structural Equation Modeling (PLS-SEM).

The results showed that factors related to diffusion play a big role in how AI gets adopted, and that adopting AI had a strong and positive impact on performance. The model explained a great amount of variance in performance. The Diffusion of Innovation and Endogenous Growth theories help explain the role of internal support and stakeholder views on AI adoption on construction projects.

This research contributes to the growing body of literature on AI in the built environment, offering insights for industry leaders and policymakers seeking to harness AI’s potential for measurable performance gains. AI offers a strategic plan to align innovation with business goals for lasting results.

Keywords: artificial intelligence, construction technology, key performance indicators, Diffusion of Innovation, technology adoption

Committee Chair/Advisor

Qiang Fei

Committee Member

Reginald Bell

Committee Member

Hesam Shahriari

Publisher

Prairie View A&M University

Rights

© 2021 Prairie View A & M University

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Date of Digitization

9/12/2025

Contributing Institution

John B Coleman Library

City of Publication

Prairie View

MIME Type

Application/PDF

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