Kim, Kwansoo

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Profile URL
https://hdl.handle.net/20.500.14365/7295
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Email Address
kwansoo.kim@ieu.edu.tr
Main Affiliation
03.02. Business Administration
Status
Former Staff
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ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
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WoS Researcher ID

Sustainable Development Goals

NO POVERTY1
NO POVERTY
1
Research Products
ZERO HUNGER2
ZERO HUNGER
0
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GOOD HEALTH AND WELL-BEING3
GOOD HEALTH AND WELL-BEING
0
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QUALITY EDUCATION4
QUALITY EDUCATION
1
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GENDER EQUALITY5
GENDER EQUALITY
1
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CLEAN WATER AND SANITATION6
CLEAN WATER AND SANITATION
0
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AFFORDABLE AND CLEAN ENERGY7
AFFORDABLE AND CLEAN ENERGY
0
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DECENT WORK AND ECONOMIC GROWTH8
DECENT WORK AND ECONOMIC GROWTH
1
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INDUSTRY, INNOVATION AND INFRASTRUCTURE9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
1
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REDUCED INEQUALITIES10
REDUCED INEQUALITIES
1
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SUSTAINABLE CITIES AND COMMUNITIES11
SUSTAINABLE CITIES AND COMMUNITIES
1
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RESPONSIBLE CONSUMPTION AND PRODUCTION12
RESPONSIBLE CONSUMPTION AND PRODUCTION
1
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CLIMATE ACTION13
CLIMATE ACTION
0
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LIFE BELOW WATER14
LIFE BELOW WATER
0
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LIFE ON LAND15
LIFE ON LAND
0
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PEACE, JUSTICE AND STRONG INSTITUTIONS16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
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PARTNERSHIPS FOR THE GOALS17
PARTNERSHIPS FOR THE GOALS
0
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Scholarly Output

2

Articles

1

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1/1

Supervised MSc Theses

0

Supervised PhD Theses

0

WoS Citation Count

38

Scopus Citation Count

49

Patents

0

Projects

0

WoS Citations per Publication

19.00

Scopus Citations per Publication

24.50

Open Access Source

1

Supervised Theses

0

JournalCount
Electronıc Commerce Research And Applıcatıons1
Proceedıngs of the 50Th Annual Hawaıı Internatıonal Conference on System Scıences1
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Now showing 1 - 2 of 2
  • Thumbnail Image
    Conference Object
    Citation - WoS: 2
    Citation - Scopus: 4
    Computational Social Science Fusion Analytics: Combining Machine-Based Methods With Explanatory Empiricism
    (Hicss, 2017) Kauffman, Robert J.; Kim, Kwansoo; Lee, Sang-Yong Tom
    This article discusses the emergence of a computational social science analytics fusion as a mainstream scientific approach involving machine-based methods and explanatory empiricism as a basis for the discovery of new policy-related insights for business, consumer and social settings. It reflects the interdisciplinary background of the new approaches that the Hawaii International Conference on Systems Science has embraced over the years, and especially some of the recent development and shifts in the scientific study of technology-related phenomena. It also has evoked new forms of research inquiry, blended approaches to research methodology, and more pointed interest in the production of research results that have direct application in various industry contexts. We review background knowledge to showcase the methods shifts, and demonstrate the new forms of research, by showcasing contemporary applications that will be interesting to the audience on the occasion of the HICSS 50th anniversary.
  • Thumbnail Image
    Article
    Citation - WoS: 36
    Citation - Scopus: 45
    Combining Machine-Based and Econometrics Methods for Policy Analytics Insights
    (Elsevier, 2017) Kauffman, Robert J.; Kim, Kwansoo; Lee, Sang-Yong Tom; Hoang, Ai-Phuong; Ren, Jing
    Computational Social Science (CSS) has become a mainstream approach in the empirical study of policy analytics issues in various domains of e-commerce research. This article is intended to represent recent advances that have been made for the discovery of new policy-related insights in business, consumer and social settings. The approach discussed is fusion analytics, which combines machine-based methods from Computer Science (CS) and explanatory empiricism involving advanced Econometrics and Statistics. It explores several efforts to conduct research inquiry in different functional areas of Electronic Commerce and Information Systems (IS), with applications that represent different functional areas of business, as well as individual consumer, social and public issues. Recent developments and shifts in the scientific study of technology-related phenomena and Social Science issues in the presence of historically-large datasets prompt new forms of research inquiry. They include blended approaches to research methodology, and more interest in the production of research results that have direct application to industry contexts. This article showcases the methods shifts and several contemporary applications. They discuss: (1) feedback effects in mobile phone-based stock trading; (2) sustainability of toprank chart popularity of music tracks; (3) household TV viewing patterns; and (4) household sampling and purchases of video-on-demand (VoD) services. The range of applicability of the ideas goes beyond the scope of these illustrations, to include issues in public services, healthcare, product and service deployment, public opinion and elections, electronic auctions, and travel and tourism services. In fact, the coverage is as broad as for-profit and for-non-profit, private and public, and governmental and non-governmental institutions. (C) 2017 Published by Elsevier B.V.