Date of Conferral

11-25-2025

Date of Award

November 2025

Degree

Doctor of Information Technology (D.I.T.)

School

Information Systems and Technology

Advisor

Nawaz Khan

Abstract

Abstract Many information technology (IT) leaders face challenges in adopting machine learning (ML) models for cloud infrastructure, despite their potential to enhance data security. The extent to which IT leaders’ perceived security (PeS) and perceived privacy (PeP) influence their intent to adopt ML security models is critical for organizational success. Grounded in the technology acceptance model, the purpose of this quantitative correlational study was to examine the relationship between IT leaders’ PeS and PeP and their intent to adopt ML security models in cloud-based applications. Data were collected from 106 IT professionals in Chicago using a validated instrument. Results from a hierarchical multiple regression analysis indicated that PeS and PeP jointly explained a significant proportion of variance in the intent to adopt ML models (R² = .536, F(8, 97) = 14.01, p < .001). Education level (β = −.012, p = .895), leadership role (β = .141, p = .122), experience at the current position (β = −.081, p = .292), and primary cloud computing strategy (β = −.060, p = .458) were not significant predictors. However, experience in cloud computing (β = −.230, p = .003) and industry type (β = .442, p < .001) were significant predictors. The findings suggest that IT leaders’ PeS and PeP, along with greater industry maturity and cloud experience, contribute to stronger intent to adopt ML security models. The implications for positive social change include the potential for organizational leaders and IT trainers to implement targeted PeS and PeP awareness programs that improve responsible data handling and cloud security practices, benefiting employees, organizations, and the public through enhanced data protection and reduced risks of security breaches.

Download

Share

 
COinS