Files and links (1)
PDFOpen Access (License Unspecified), Open Access
Dissertation
A quantitative study of generative AI advocacy intention determinants
Doctor of Business Administration (D.B.A.), Drexel University
Feb 2025
DOI:
https://doi.org/10.17918/00010876
Abstract
Generative artificial intelligence (GenAI) represents a pivotal innovation in technology, transforming processes through advanced automation and content generation. This study investigates the dynamics of GenAI adoption, focusing on trust, distrust, and legitimacy as key constructs influencing advocacy intentions. The research initially proposed an experimental conceptual model that hypothesized trust and distrust as simultaneous mediators between legitimacy dimensions (cognitive, pragmatic, normative, and regulative) and advocacy intentions, moderated by risk contexts. However, empirical findings did not substantiate the proposed pathways, prompting a revision of the framework. Through a quantitative survey methodology, the study provides a re-evaluation of legitimacy's role and its impact on GenAI adoption behaviors across low-risk and high-risk application scenarios. By addressing these complexities, this research contributes to the scholarly discourse on GenAI advocacy, offering actionable insights for organizations aiming to integrate GenAI responsibly while navigating challenges in trust and legitimacy across diverse contexts.
Metrics
302 File views/ downloads
42 Record Views
Details
- Title
- A quantitative study of generative AI advocacy intention determinants
- Creators
- Albert Horace Harper III
- Contributors
- David Gefen (Advisor)
- Awarding Institution
- Drexel University
- Degree Awarded
- Doctor of Business Administration (D.B.A.)
- Publisher
- Drexel University; Philadelphia, Pennsylvania
- Number of pages
- xii, 162 pages
- Resource Type
- Dissertation
- Language
- English
- Academic Unit
- Bennett S. LeBow College of Business; Drexel University
- Other Identifier
- 991022040072804721