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The Technology Adoption Model for Cloud Computing, Storytelling Artificial Intelligence, and the Federal Risk and Authorization Management Program.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	The Technology Adoption Model for Cloud Computing, Storytelling Artificial Intelligence, and the Federal Risk and Authorization Management Program./
Author:	Jackson, Freeman Augustus.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
Description:	271 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-09, Section: B.
Contained By:	Dissertations Abstracts International85-09B.
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30640477
ISBN:	9798381947045

The Technology Adoption Model for Cloud Computing, Storytelling Artificial Intelligence, and the Federal Risk and Authorization Management Program.
Jackson, Freeman Augustus.

The Technology Adoption Model for Cloud Computing, Storytelling Artificial Intelligence, and the Federal Risk and Authorization Management Program. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 271 p.

Source: Dissertations Abstracts International, Volume: 85-09, Section: B.

Thesis (D.C.S.)--Aspen University, 2024.

This item must not be sold to any third party vendors.

This dissertation examines challenges and opportunities in federal government Conversational AI and Machine Learning (CAIML) integration. It emphasizes Conversational AI (CAI)'s impact on decision-making across sectors and its improvement of human-technology interactions, particularly in IoT and cloud computing. The study reviews AI storytelling, including NLP, character generation, VR/AR, deep learning, leadership, and FedRAMP compliance.Qualitative research follows PRISMA and uses NVivo for data analysis. A comprehensive literature review, qualitative analysis, and expert interviews reveal CAIML adoption challenges and opportunities. Leadership, economic and legal factors, privacy, data protection, and national security are studied. The dissertation concludes with a summary of its findings and research questions on federal agency CAI and ML adoption barriers, operational improvements, legal/regulatory issues, privacy, data protection, security threats, and effective leadership strategies. CAI and ML integration into federal infrastructure and national security and intelligence implications are also discussed. The dissertation recommends federal agencies prioritize personnel training, migration planning, and solution sustainability to advance CAIML adoption. It helps federal policymakers and practitioners adopt CAIML technologies by highlighting their transformative potential and challenges.

ISBN: 9798381947045Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Conversational AI and Machine Learning

The Technology Adoption Model for Cloud Computing, Storytelling Artificial Intelligence, and the Federal Risk and Authorization Management Program.
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520 $a This dissertation examines challenges and opportunities in federal government Conversational AI and Machine Learning (CAIML) integration. It emphasizes Conversational AI (CAI)'s impact on decision-making across sectors and its improvement of human-technology interactions, particularly in IoT and cloud computing. The study reviews AI storytelling, including NLP, character generation, VR/AR, deep learning, leadership, and FedRAMP compliance.Qualitative research follows PRISMA and uses NVivo for data analysis. A comprehensive literature review, qualitative analysis, and expert interviews reveal CAIML adoption challenges and opportunities. Leadership, economic and legal factors, privacy, data protection, and national security are studied. The dissertation concludes with a summary of its findings and research questions on federal agency CAI and ML adoption barriers, operational improvements, legal/regulatory issues, privacy, data protection, security threats, and effective leadership strategies. CAI and ML integration into federal infrastructure and national security and intelligence implications are also discussed. The dissertation recommends federal agencies prioritize personnel training, migration planning, and solution sustainability to advance CAIML adoption. It helps federal policymakers and practitioners adopt CAIML technologies by highlighting their transformative potential and challenges.
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