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Digital Transformation in the Europe...
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Nguyen, Owen Joseph.
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Digital Transformation in the European Union: Maximizing GDP Per Capita Through Predictive Modeling.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Digital Transformation in the European Union: Maximizing GDP Per Capita Through Predictive Modeling./
作者:
Nguyen, Owen Joseph.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2024,
面頁冊數:
142 p.
附註:
Source: Dissertations Abstracts International, Volume: 85-06, Section: A.
Contained By:
Dissertations Abstracts International85-06A.
標題:
Engineering. -
電子資源:
https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30815567
ISBN:
9798381175271
Digital Transformation in the European Union: Maximizing GDP Per Capita Through Predictive Modeling.
Nguyen, Owen Joseph.
Digital Transformation in the European Union: Maximizing GDP Per Capita Through Predictive Modeling.
- Ann Arbor : ProQuest Dissertations & Theses, 2024 - 142 p.
Source: Dissertations Abstracts International, Volume: 85-06, Section: A.
Thesis (D.Engr.)--The George Washington University, 2024.
This item must not be sold to any third party vendors.
The European Union (EU) confronts a critical challenge as only 55% of small and medium-sized enterprises have attained a fundamental level of digital technology adoption, potentially forfeiting 490 billion euros from unrealized digitalization plans. A compelling solution emerges in the form of a predictive model, guiding the allocation of EU digital investments to maximize gross domestic product (GDP) per capita.This study presents a comprehensive analysis examining the dynamic interplay between technology and economic performance. It inspects the correlation between technology and economic performance, identifies regional variations, investigates the role of government in shaping technological progress, and creates a predictive model to optimize digital investments for economic prosperity. Several analytical methods were implemented, including Pearson and Spearman for correlation analysis, analysis of variance (ANOVA) for group comparisons, and multiple linear regression and decision tree analyses for machine learning.The results underscore the pivotal role of technology in affecting economic performance, urging policymakers and industry leaders to allocate resources judiciously. They also emphasize the significance of integrated digital technologies in digital transformation projects, highlight the inferior progress of digital transformation in Eastern Europe, and show a positive correlation between digital government services and economic growth. Moreover, the work on predictive modeling has unveiled the possibility of forecasting the impact of digital transformation on GDP per capita, which can empower decision-makers with a tool to make data-driven choices and optimize their digital strategies.Recommendations for future research include developing customized models for each EU nation, considering long-term economic resilience, exploring the behavioral aspects of data privacy and security, investigating the readiness of the EU workforce to adapt to emerging technological trends, and conducting studies of select countries or regions with successful digital transformation initiatives and comparing them to the EU.
ISBN: 9798381175271Subjects--Topical Terms:
586835
Engineering.
Subjects--Index Terms:
Digital economy
Digital Transformation in the European Union: Maximizing GDP Per Capita Through Predictive Modeling.
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The European Union (EU) confronts a critical challenge as only 55% of small and medium-sized enterprises have attained a fundamental level of digital technology adoption, potentially forfeiting 490 billion euros from unrealized digitalization plans. A compelling solution emerges in the form of a predictive model, guiding the allocation of EU digital investments to maximize gross domestic product (GDP) per capita.This study presents a comprehensive analysis examining the dynamic interplay between technology and economic performance. It inspects the correlation between technology and economic performance, identifies regional variations, investigates the role of government in shaping technological progress, and creates a predictive model to optimize digital investments for economic prosperity. Several analytical methods were implemented, including Pearson and Spearman for correlation analysis, analysis of variance (ANOVA) for group comparisons, and multiple linear regression and decision tree analyses for machine learning.The results underscore the pivotal role of technology in affecting economic performance, urging policymakers and industry leaders to allocate resources judiciously. They also emphasize the significance of integrated digital technologies in digital transformation projects, highlight the inferior progress of digital transformation in Eastern Europe, and show a positive correlation between digital government services and economic growth. Moreover, the work on predictive modeling has unveiled the possibility of forecasting the impact of digital transformation on GDP per capita, which can empower decision-makers with a tool to make data-driven choices and optimize their digital strategies.Recommendations for future research include developing customized models for each EU nation, considering long-term economic resilience, exploring the behavioral aspects of data privacy and security, investigating the readiness of the EU workforce to adapt to emerging technological trends, and conducting studies of select countries or regions with successful digital transformation initiatives and comparing them to the EU.
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