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Empowering, Engaging, and Equipping ...

Booker, Mario DeSean.

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Empowering, Engaging, and Equipping Technology through Acceptance: A Quantitative Study Utilizing the Modified Technology Acceptance Model (mTAM) to Explore Facial-Recognition Software Acceptance (FRSA) in the Smart Policing Initiative (SPI).

Record Type:	Electronic resources : Monograph/item
Title/Author:	Empowering, Engaging, and Equipping Technology through Acceptance: A Quantitative Study Utilizing the Modified Technology Acceptance Model (mTAM) to Explore Facial-Recognition Software Acceptance (FRSA) in the Smart Policing Initiative (SPI)./
Author:	Booker, Mario DeSean.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
Description:	140 p.
Notes:	Source: Dissertations Abstracts International, Volume: 84-07, Section: B.
Contained By:	Dissertations Abstracts International84-07B.
Subject:	Information technology. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30001296
ISBN:	9798368418933

Empowering, Engaging, and Equipping Technology through Acceptance: A Quantitative Study Utilizing the Modified Technology Acceptance Model (mTAM) to Explore Facial-Recognition Software Acceptance (FRSA) in the Smart Policing Initiative (SPI).
Booker, Mario DeSean.

Empowering, Engaging, and Equipping Technology through Acceptance: A Quantitative Study Utilizing the Modified Technology Acceptance Model (mTAM) to Explore Facial-Recognition Software Acceptance (FRSA) in the Smart Policing Initiative (SPI). - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 140 p.

Source: Dissertations Abstracts International, Volume: 84-07, Section: B.

Thesis (Ph.D.)--University of the Cumberlands, 2022.

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

The Smart Police Initiative (SPI) aims to incorporate fundamental technology elements into policing activities to increase productivity, the accuracy of arrest and detainment, and improve caseload management. This research aims to integrate the modified Technology Acceptance Model (mTAM) framework to explore the factors contributing to the Smart Policing Initiative's decision to accept Facial-Recognition Software. This quantitative non-experimental correlational study evaluates the relationship between the Perceived Ease-Of-Use (PEOU), Perceived Usefulness (PU), and acceptance of Facial-Recognition Software in the Smart Policing Initiative. Inadequate research examining how PEOU and PU can impact acceptance into the SPI represents a gap this study aims to address. The research addresses the need for a conversation to explore how the SPI works in collaboration with the acceptance of Facial-Recognition Software. Data collection for this study utilized the modified Technology Acceptance Model survey, which was a pre-established survey. A Pearson's correlation coefficient analysis investigated the relationship between the independent and dependent variables. Pearson's correlation coefficient was performed to ascertain the correlation between the dependent and independent variables. Within this study, there was one dependent variable and two independent variables. The independent variables for this study are perceived ease-of-use (PEOU) and perceived usefulness (PU), and the dependent variable is life cycle technology adapters (LCTA) of FRS. The finding of this study provided statistical evidence that there exists a significant relationship between PEOU, PU, and LCTA.Regarding the relationship between PEOU and LCTA of FRS, this study found a significant difference between these two variables, rejecting the null hypothesis and suggesting that the relationship between the two variables was significant. Furthermore, the null hypothesis was subsequently rejected when the relationship between PU and LCTA was investigated. The results show that the relationships between these variables are significant. Regarding the correlation between LCTA from FRS to PEOU and PU, the results show that the data support the alternative hypothesis with a statistical test p-value of less than 0.05. Therefore, the null hypothesis was rejected. This study concludes that the relationship between LCTA, PEOU, and PU significantly affects FRS adoption into the SPI. It can be concluded that the overall goal of the results from the study to be impactful to the Smart Policing Initiative and the agencies in the police fields that are looking to implement Facial-Recognition Software was completed satisfactorily. The research contributed to the scarcity of literature exploring how the SPI works in collaboration with the acceptance of Facial-Recognition Software and the attributing factors toward its adoption and acceptance.

ISBN: 9798368418933Subjects--Topical Terms:

532993
Information technology.
Subjects--Index Terms:

Clearview AI

Empowering, Engaging, and Equipping Technology through Acceptance: A Quantitative Study Utilizing the Modified Technology Acceptance Model (mTAM) to Explore Facial-Recognition Software Acceptance (FRSA) in the Smart Policing Initiative (SPI).
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500 $a Source: Dissertations Abstracts International, Volume: 84-07, Section: B.
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520 $a The Smart Police Initiative (SPI) aims to incorporate fundamental technology elements into policing activities to increase productivity, the accuracy of arrest and detainment, and improve caseload management. This research aims to integrate the modified Technology Acceptance Model (mTAM) framework to explore the factors contributing to the Smart Policing Initiative's decision to accept Facial-Recognition Software. This quantitative non-experimental correlational study evaluates the relationship between the Perceived Ease-Of-Use (PEOU), Perceived Usefulness (PU), and acceptance of Facial-Recognition Software in the Smart Policing Initiative. Inadequate research examining how PEOU and PU can impact acceptance into the SPI represents a gap this study aims to address. The research addresses the need for a conversation to explore how the SPI works in collaboration with the acceptance of Facial-Recognition Software. Data collection for this study utilized the modified Technology Acceptance Model survey, which was a pre-established survey. A Pearson's correlation coefficient analysis investigated the relationship between the independent and dependent variables. Pearson's correlation coefficient was performed to ascertain the correlation between the dependent and independent variables. Within this study, there was one dependent variable and two independent variables. The independent variables for this study are perceived ease-of-use (PEOU) and perceived usefulness (PU), and the dependent variable is life cycle technology adapters (LCTA) of FRS. The finding of this study provided statistical evidence that there exists a significant relationship between PEOU, PU, and LCTA.Regarding the relationship between PEOU and LCTA of FRS, this study found a significant difference between these two variables, rejecting the null hypothesis and suggesting that the relationship between the two variables was significant. Furthermore, the null hypothesis was subsequently rejected when the relationship between PU and LCTA was investigated. The results show that the relationships between these variables are significant. Regarding the correlation between LCTA from FRS to PEOU and PU, the results show that the data support the alternative hypothesis with a statistical test p-value of less than 0.05. Therefore, the null hypothesis was rejected. This study concludes that the relationship between LCTA, PEOU, and PU significantly affects FRS adoption into the SPI. It can be concluded that the overall goal of the results from the study to be impactful to the Smart Policing Initiative and the agencies in the police fields that are looking to implement Facial-Recognition Software was completed satisfactorily. The research contributed to the scarcity of literature exploring how the SPI works in collaboration with the acceptance of Facial-Recognition Software and the attributing factors toward its adoption and acceptance.
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