Information and Communications Technology (ICT) network readiness competency improves service quality and provides efficient service in implementing successful e-governments. By confirming ICT network readiness of e-governments, it must be redesigned using limited resources effectively to achieve realistic goals. When ICT investment and economic performance are featured, e-government’s network readiness competency improves potential demand, supply, and service maturity. It reflects information technology (IT) development competency on performance effectively. In this study, we propose the Data Envelope Analysis (DEA) method to present a method of improving ICT network readiness between countries. We derived the ICT network’s readiness competency level and strategic plan by comparing each country for efficient ICT operation of e-governments. If we make rankings in a non-traditional and efficient manner, it will become a successful strategy for ICT in the future. This effort provides guidance for each government and a solution for the growth delay problem, which is required for advancement in ICT investment and productivity. It also guides each government to overcome marginal products.
The world is now strengthening its Information and Communication Technology (ICT) capabilities to secure economic growth and national competitiveness. The role of ICT is important for problems like COVID-19. ICT based innovation is effective in responding to problems for industry, economy, and society. However, we need to understand, not from the perspective of performance or investment, that the use and performance of ICT technology are promoted when each country’s ICT related environment, policies, governance, and regulations are effective. We need to share sustainable ICT experiences, successes, and challenges to solve complex problems and reorganize policies. This study proposes a Text Mining methodology from a future-oriented perspective to extract semantic system patterns from International Telecommunication Union (ITU) professional reports. In the text extracted from the report, we found a new relationship pattern and a potential topic. The research results provide insights into a diverse perspective for policymakers to search for successful ICT strategies.
The COVID-19 pandemic has affected smart city operations and planning. Smart cities, where digital technologies are concentrated and implemented, face new challenges in becoming sustainable from social, ecological, and economic perspectives. Using text mining methodologies of topic modeling and network analysis, this study aims to identify keywords in the field of smart cities after the pandemic and provide a future-oriented perspective on the direction of smart cities. A corpus of 1882 papers was collected from the Web of Science and Scopus databases from December 2019 to November 2022. We identified six categories of potential issues in smart cities using topic modeling: “supply chain”, “resilience”, “culture and tourism”, “population density”, “mobility”, and “zero carbon emission”. This study differs from previous research because it is a quantitative study based on text mining analysis and deals with smart cities, given the prevalence of COVID-19. This study also provides insights into the development of smart city policies and strategies to improve urban resilience during the pandemic by anticipating and addressing related issues. The findings of this study will assist researchers, policymakers, and planners in developing smart city strategies and decision-making in socioeconomic, environmental, and technological areas.
This study aims to understand the global environment of COVID-19 management and guide future policy directions after the pandemic crisis. To this end, we analyzed a series of the World Economic Forum’s COVID-19 response reports through text mining and network analysis. These reports, written by experts in diverse fields, discuss multidimensional changes in socioeconomic situations, various problems created by those changes, and strategies to respond to national crises. Based on 3897 refined words drawn from a morphological analysis of 26 reports (as of the end of 2020), this study analyzes the frequency of words, the relationships among words, the importance of specific documents, and the connection centrality through text mining. In addition, the network analysis helps develop strategies for a sustainable response to and the management of national crises through identifying clusters of words with similar structural equivalence.
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