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Using Emerging Blockchain Technologies and Smart Contracts to Improve Business Efficiencies Through AI Algorithms.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Using Emerging Blockchain Technologies and Smart Contracts to Improve Business Efficiencies Through AI Algorithms.
Author:	Foley, Brandon C.
Published:	Ann Arbor : ProQuest Dissertations & Theses, 2021
Description:	104 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-05, Section: B.
Notes:	Advisor: Lipinski, John.
Contained By:	Dissertations Abstracts International83-05B.
Subject:	Business administration.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28773585
ISBN:	9798496507356

Using Emerging Blockchain Technologies and Smart Contracts to Improve Business Efficiencies Through AI Algorithms.
Foley, Brandon C.

Using Emerging Blockchain Technologies and Smart Contracts to Improve Business Efficiencies Through AI Algorithms. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 104 p.

Source: Dissertations Abstracts International, Volume: 83-05, Section: B.

Thesis (Ph.D.)--Indiana University of Pennsylvania, 2021.

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

This paper presents a longitudinal study that looks at business efficiencies in the oil and gas sector and the supply chain (SC) from past, present, and future research perspectives. The paper examines previous research in oil and gas which analyzed the influence that the six independent variables of gas price, cost to turn gas on, aggregated gas cost, operating expenses, cost to drill new wells, and royalties had on the dependent variable demand for natural gas. This study extends these previous research efforts with a larger, longitudinal dataset to understand its significance and to determine if the research is grounded in academic literature. The results of the study suggest that the multiple regression analysis is statistically significant and adds value to academic research in the oil and gas SC.The paper then moves focus to the modern topic of sustainability in the SC and examines previous research on sustainability and triple bottom line (TBL) in the SC. Ideas from one study show that three of the six independent variables from the longitudinal dataset, royalties, gas price, and operating expenses can be linked to the three components of TBL, which are people, profit, and planet, respectively (Rodger & George, 2017). With principal component analysis and factor rotation, the study presents evidence that supports this and shows these variables have a strong influence on SC efficiency.Next, a literature review of blockchain technology and smart contracts is conducted to help create an understanding of future needs in SC research. Evidence supports that blockchain technology will be used in place of past technologies to run the SC. A model is developed to view the SC and blockchain through the world of the natural sciences. The model utilizes the nine waves of SC sustainability to predict the entropy of robotic blockchain zero-point energy in the natural gas SC and employs nearest neighbor fuzzy modular functions to further investigate this phenomenon using a truncated dataset of robotic supply blockchains from the genesis block. Three hypotheses are developed:1.)Energy in the SC void can be improved, and entropy decreased, through the adoption of modern technologies like blockchain.2.)Energy in the SC void can be improved, and entropy decreased, through the adoption of physics principles.3.)Energy in the SC void can be improved, and entropy decreased, through better cybersecurity technologies.Bayes’ density estimation fuzzy modular method is employed to reduce type I and type II errors and render the nearest neighbor clustering and entropy density more accurately throughout the nine waves of SC sustainability. The expectation is that if the input, output, and trans blockchains all possess high energy and low zero-point energy modular density estimates, the blockchain will be error-free and self-heal from any attempts to add fraudulent hash algorithms. It is established that the use of the robotic blockchain may have a causal relationship with improving natural gas SC resource efficiency in the future. With these findings, the paper concludes that the three hypotheses are supported.

ISBN: 9798496507356Subjects--Topical Terms:

708619
Business administration.
Subjects--Index Terms:

Artificial intelligence

Using Emerging Blockchain Technologies and Smart Contracts to Improve Business Efficiencies Through AI Algorithms.
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500 $a Source: Dissertations Abstracts International, Volume: 83-05, Section: B.
500 $a Advisor: Lipinski, John.
502 $a Thesis (Ph.D.)--Indiana University of Pennsylvania, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a This paper presents a longitudinal study that looks at business efficiencies in the oil and gas sector and the supply chain (SC) from past, present, and future research perspectives. The paper examines previous research in oil and gas which analyzed the influence that the six independent variables of gas price, cost to turn gas on, aggregated gas cost, operating expenses, cost to drill new wells, and royalties had on the dependent variable demand for natural gas. This study extends these previous research efforts with a larger, longitudinal dataset to understand its significance and to determine if the research is grounded in academic literature. The results of the study suggest that the multiple regression analysis is statistically significant and adds value to academic research in the oil and gas SC.The paper then moves focus to the modern topic of sustainability in the SC and examines previous research on sustainability and triple bottom line (TBL) in the SC. Ideas from one study show that three of the six independent variables from the longitudinal dataset, royalties, gas price, and operating expenses can be linked to the three components of TBL, which are people, profit, and planet, respectively (Rodger & George, 2017). With principal component analysis and factor rotation, the study presents evidence that supports this and shows these variables have a strong influence on SC efficiency.Next, a literature review of blockchain technology and smart contracts is conducted to help create an understanding of future needs in SC research. Evidence supports that blockchain technology will be used in place of past technologies to run the SC. A model is developed to view the SC and blockchain through the world of the natural sciences. The model utilizes the nine waves of SC sustainability to predict the entropy of robotic blockchain zero-point energy in the natural gas SC and employs nearest neighbor fuzzy modular functions to further investigate this phenomenon using a truncated dataset of robotic supply blockchains from the genesis block. Three hypotheses are developed:1.)Energy in the SC void can be improved, and entropy decreased, through the adoption of modern technologies like blockchain.2.)Energy in the SC void can be improved, and entropy decreased, through the adoption of physics principles.3.)Energy in the SC void can be improved, and entropy decreased, through better cybersecurity technologies.Bayes’ density estimation fuzzy modular method is employed to reduce type I and type II errors and render the nearest neighbor clustering and entropy density more accurately throughout the nine waves of SC sustainability. The expectation is that if the input, output, and trans blockchains all possess high energy and low zero-point energy modular density estimates, the blockchain will be error-free and self-heal from any attempts to add fraudulent hash algorithms. It is established that the use of the robotic blockchain may have a causal relationship with improving natural gas SC resource efficiency in the future. With these findings, the paper concludes that the three hypotheses are supported.
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