Examining the Relationship between Artificial Intelligence Features and Financial Performance Among Non-Financial Firms
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Authors
MORENO BONILLA, CARLOS
Issue Date
2025-09
Type
Dissertation
Language
en
Keywords
AI impact on business financial performance , AI predictive relationship with financial ratios , Artificial Neural Network used to improve the firm's financial metrics , Student Success Science , Business, Engineering, Science, & Technological Innovation , Workforce Development Needs & Industry Alignment
Alternative Title
Abstract
The problem addressed in this study was the barrier among non-financial firms (NFFs) to incorporate AI features (AIFs) driven by the lack of knowledge and empirical evidence linking AIFs usage to tangible financial outcomes. This gap hindered firms’ understanding of how AIFs adoption affects financial performance indicators (FPIs), particularly in big data and Internet of Things (IoT), adversely impacting their operational efficiency and sustained competitiveness. The purpose of this quantitative correlational study was to examine the predictive relationship between AIFs and FPIs among NFFs operating within these technological contexts. The conceptual framework is depicted from the artificial neural network (ANN) theory, stressing the strategic value of AIFs in optimizing financial results. Using Qualtrics, data was collected through survey responses from 211 technology-sector NFFs listed in the Nasdaq Composite Index. Additionally, 77 secondary sources were used to identify, through mode statistics, the 12 core financial ratios that represent the firm’s FPIs. Three models were built using Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess the predictive relationship between AIFs and FPIs: (a) an indirect model with six latent mediators, Functional Automation Constructs (FACs), (b) a refined indirect model excluding weak predictors in the previous model, and (c) a direct model linking AIFs to increases in FPIs. Descriptive statistics, principal component analysis, and bootstrapping confirmed models’ reliability and statistical significance. Results revealed that two AIF dimensions - years of use and processes integration level – had consistent and statistically significant predictive relationships with FPIs. This study offers theoretical and practical contributions by highlighting how AIFs maturity and organizational integration enhance financial performance and resilience in NFFs. The findings support digital strategy development and investment planning. Future research should explore alternative modeling strategies, longitudinal designs, and cross-industry comparisons to validate and expand on these findings.