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Improving Technology Acceptance Mode...

Fletcher, Jordan L.

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Improving Technology Acceptance Modeling For Disadvantaged Communities Using a Systems Engineering Approach.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Improving Technology Acceptance Modeling For Disadvantaged Communities Using a Systems Engineering Approach./
Author:	Fletcher, Jordan L.
Description:	135 p.
Notes:	Source: Dissertation Abstracts International, Volume: 74-11(E), Section: B.
Contained By:	Dissertation Abstracts International74-11B(E).
Subject:	Engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3589422
ISBN:	9781303279799

Improving Technology Acceptance Modeling For Disadvantaged Communities Using a Systems Engineering Approach.
Fletcher, Jordan L.

Improving Technology Acceptance Modeling For Disadvantaged Communities Using a Systems Engineering Approach. - 135 p.

Source: Dissertation Abstracts International, Volume: 74-11(E), Section: B.

Thesis (Ph.D.)--The George Washington University, 2013.

Developing nations are poised to spend billions on information and communication technology (ICT) innovation in 2020. A study of the historical adoption of ICT in developing nations has indicated that their adoption patterns do not follow typical technology innovation adoption models. This study addressed the weaknesses found in existing technology acceptance models and proposed a new approach and conceptual model for technology acceptance. The new model integrated the population and technology characteristics factors found in popular technology acceptance models to task and process factors found in other existing models, consistent with System Engineering principles and Information Technology Infrastructure Library frameworks. This study also addressed the weakness in the survey approach by utilizing data from publicly-available industry and economic data sources. The model was validated in two experiments, featuring multiple regression tests against different data sets. The first experiment applied the model to mobile phone technology adoption in 42 nations in Sub-Saharan Africa. The second experiment applied the model to broadband internet adoption by 338 firms in India. The experiments found that technology use by disadvantaged user populations was significantly correlated to relative advantage, technology accessibility, relative cost, and process support infrastructure. The new model and approach should be further validated by future research.

ISBN: 9781303279799Subjects--Topical Terms:

586835
Engineering.

Improving Technology Acceptance Modeling For Disadvantaged Communities Using a Systems Engineering Approach.
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500 $a Source: Dissertation Abstracts International, Volume: 74-11(E), Section: B.
500 $a Advisers: Shahram Sarkani; Thomas A. Mazzuchi.
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520 $a Developing nations are poised to spend billions on information and communication technology (ICT) innovation in 2020. A study of the historical adoption of ICT in developing nations has indicated that their adoption patterns do not follow typical technology innovation adoption models. This study addressed the weaknesses found in existing technology acceptance models and proposed a new approach and conceptual model for technology acceptance. The new model integrated the population and technology characteristics factors found in popular technology acceptance models to task and process factors found in other existing models, consistent with System Engineering principles and Information Technology Infrastructure Library frameworks. This study also addressed the weakness in the survey approach by utilizing data from publicly-available industry and economic data sources. The model was validated in two experiments, featuring multiple regression tests against different data sets. The first experiment applied the model to mobile phone technology adoption in 42 nations in Sub-Saharan Africa. The second experiment applied the model to broadband internet adoption by 338 firms in India. The experiments found that technology use by disadvantaged user populations was significantly correlated to relative advantage, technology accessibility, relative cost, and process support infrastructure. The new model and approach should be further validated by future research.
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