Date of Award

Fall 12-6-2024

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computer Information Systems

First Advisor

Dr. Balasubramaniam Ramesh

Second Advisor

Dr. J.J. Hsieh

Third Advisor

Dr. Likoebe Maruping

Fourth Advisor

Dr. Liwei Chen

Abstract

This dissertation explores the transformative potential of low-code technologies in application development, focusing on their complexity reduction mechanisms, affordances, and effective use. The first study synthesizes low-code technologies—low-code SaaS, low-code application platforms, and generative AI programming assistants—using Parnas’ information-hiding framework to map these affordances to Wood’s task complexities. This analysis highlights shared reuse, abstraction, and automation patterns, showing how low-code reduces development task complexity and expands access for non-technical users. Building on these findings, the second study examines the effective use of low-code application platforms (LCAPs) through the lens of the theory of effective use (TEU). By analyzing how user, task, and system elements interact, the study identifies critical antecedents to effective use, including functional affordances, requirements quality, and user-centered design. Empirical evidence supports these factors as enablers of effective low-code development and extends the TEU framework to software engineering contexts. Together, these studies provide a foundation for understanding low-code technologies and their implications for application development. They offer theoretical and practical insights into reducing development complexity, improving system use, and advancing software engineering practices in the era of low-code innovation.

File Upload Confirmation

1

Download

Share

COinS