Files and links (1)
PDFOpen Access (License Unspecified), Open Access
Dissertation
Trust in the Machine: AI-Powered Tax Services vs. Human Experts
Doctor of Business Administration (D.B.A.), Drexel University
Jun 2024
DOI:
https://doi.org/10.17918/00010429
Abstract
Integrating Artificial Intelligence (AI) into digital automation technologies is revolutionizing numerous industries and business models. This research focuses on AI's role in creating digital workers for tax advice while exploring the differences and similarities of interacting with an AI digital worker compared to a human CPA. Digital workers, enabled by AI, are poised to significantly impact the white-collar workforce, particularly in automating routine tasks and enhancing efficiency in areas like financial advice, healthcare, and customer service. Advancements in AI, notably in large language models like ChatGPT, have opened new avenues for applications such as chatbots and virtual assistants. These AI-driven digital workers utilize technologies like Robotic Process Automation (RPA) and analytics, automating business functions and augmenting human interaction. Digital worker automation, combining conversational intelligence and RPA, works alongside employees, understanding and responding to commands. These workers represent metaverse-based extensions of digital assistants like Amazon's Alexa and Apple's Siri. The technology enabling digital workers is rapidly progressing, creating significant market opportunities and potentially disrupting white-collar job roles. A critical aspect of this research is understanding the factors influencing customers' willingness to share sensitive information with AI-driven digital workers compared to human experts. The study examines the broader adoption and utilization of digital workers, considering demographic factors and their impact on technology adoption. Theoretical frameworks like the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) are used to analyze trust in digital workers. The study also explores the impact of perceived anthropomorphism, intelligence, and animacy on AI adoption and the level of technostress experienced by users. In summary, this research provides insights into the potential and challenges of employing AI-driven digital workers in specialized fields like tax advice. It aims to extend an understanding of how digital worker technology can be adopted and utilized effectively, studying both benefits and limitations.
Metrics
63 File views/ downloads
51 Record Views
Details
- Title
- Trust in the Machine
- Creators
- Barry Anderson Leffew
- Contributors
- David Gefen (Advisor)Murugan Anandarajan (Advisor) - Drexel University, Decision Sciences (and Management Information Systems)
- Awarding Institution
- Drexel University
- Degree Awarded
- Doctor of Business Administration (D.B.A.)
- Publisher
- Drexel University; Philadelphia, Pennsylvania
- Number of pages
- xv, 168 pages
- Resource Type
- Dissertation
- Language
- English
- Academic Unit
- Bennett S. LeBow College of Business; Drexel University
- Other Identifier
- 991021877012704721