JiHo Hwang, YoungJun Kim, Soekho Son and Jongmin Han
Accurate and timely foresight of future trends and changes in science and technology (S&T) is a key to national competitiveness. The purpose of this paper is to describe recent…
Abstract
Purpose
Accurate and timely foresight of future trends and changes in science and technology (S&T) is a key to national competitiveness. The purpose of this paper is to describe recent technological foresight conducted by the Technology Foresight Center at the Korea Institute of Science and Technology Evaluation and Planning. The authors also address current issues and problems encountered in the process of technology foresight (TF) in Korea.
Design/methodology/approach
The authors used TF methodologies which included bibliometrics, expert panels, SWOT analysis, and conferences/seminars.
Findings
The authors derived 20 future issues and 40 future needs in S&T from the five areas of global mega trends; then 200 future technologies were suggested with a time horizon of 40 years, from 2010 to 2050, for solving those future issues and needs. Finally, the authors suggested outlooks for technological development in the years 2020 and 2050.
Research limitations/implications
It is time to evaluate Korean TF exercises which were started 15 years ago. The evaluation of TF will help guide TF and inform policy and decision makers.
Practical implications
The findings can help policy makers shape future governmental S&T policy in Korea. The final result of this work was applied to the second S&T Framework Plan in Korea, in which 90 technologies were identified as national key technologies for the next five years, from 2008 to 2012.
Originality/value
To the best of the authors' knowledge, there are no studies suggesting not only future technologies based on solving future issues and needs but also outlooks for technology development in the years 2020 and 2050.
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Abstract
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Sungwon Oh, Min Jae Park, Tae You Kim and Jiho Shin
This study aimed to present the methodology of the text data analysis to establish marketing strategies for fintech companies in a practical way. Specifically, the methodology was…
Abstract
Purpose
This study aimed to present the methodology of the text data analysis to establish marketing strategies for fintech companies in a practical way. Specifically, the methodology was presented to convert customers' review data, which consisted of the text data (unstructured data), to the numerical data (structured data) by using a text mining algorithm “Global Vectors for Word Representation,” abbreviated as “GloVe”; additionally, the authors presented the methodology to deploy the numerical data for marketing strategies with eliminate-reduce-raise-create (ERRC) value factor analytics.
Design/methodology/approach
First, the authors defined the background, features and contents of fintech services based on a review of related literature review. Additionally, they examined business strategies, the importance of social media for fintech services and fintech technology trends based on the literature review. Next, they analyzed the similarity between fintech-related keywords, which represent the trends in fintech services, and the text data related to fintech corporations and their services posted on Facebook and Twitter, which are two of the most popular social media globally, during the period 2017–2019. The similarity was then quantified and categorized in terms of the representative global fintech companies and the status of each fintech service sector. Furthermore, the similarity was visualized, and value elements were rebuilt using ERRC strategy analytics.
Findings
This study is meaningful in that it quantifies the degree of similarity between customers' responses, experiences and expectations regarding the rapidly growing global fintech firms' services and trends in fintech services.
Originality/value
This study suggests a practical way to apply in business by providing a method for transforming unstructured text data into structured numerical data it is measurable. It is expected that this study can be used as the basis for exploring sustainable development strategies for the fintech industry.