Jeonghwan Jeon

Prof. Jeonghwan Jeon

Gyeongsang National University
orcid

Research Focus & Metrics

Academic Career Length

19Years (2008-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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MLMinHwa Lee
MinHwa Lee
Jinhyo Joseph YunJinhyo Joseph Yun
Jinhyo Joseph Yun
APAndreas Pyka
Andreas Pyka
DWDongkyu Won
Dongkyu Won
FKFumio Kodama
Fumio Kodama
XIAOFEI ZHAOXIAOFEI ZHAO
XIAOFEI ZHAO
Since Klaus Schwab and the World Economic Forum declared the arrival of the Fourth Industrial Revolution, there has been much discussion about it. However, there is no commonly agreed-upon definition of the Fourth Industrial Revolution. Therefore, we attempted to answer the following four research questions. “What is the definition of the Fourth Industrial Revolution?”, “How can we respond to the Fourth Industrial Revolution in terms of institutions?”, “How can we respond to the Fourth Industrial Revolution in terms of technology?”, “How can we respond to the Fourth Industrial Revolution in terms of firm innovation and start-up strategy?” Brainstorming was conducted by 11 scholars from several countries to answer these four research questions. Therefore, this research is not the end product of four research questions, but a kind of advanced template to answer the four research questions for continuing research.

1 May 2011 in Technological Forecasting and Social Change
Verified
Journal Article
Closed access
CLChangyong Lee
Changyong Lee
JJJeonghwan Jeon
Jeonghwan Jeon
YPYongtae Park
Yongtae Park

1 February 2012 in Expert Systems with Applications
Verified
Journal Article
Closed access
CSChangho Son
Changho Son
YSYongyoon Suh
Yongyoon Suh
JJJeonghwan Jeon
Jeonghwan Jeon
YPYongtae Park
Yongtae Park

15 October 2015 in Technology Analysis & Strategic Management
Verified
Journal Article
Closed access
SLSanghoon Lee
Sanghoon Lee
WKWonjoon Kim
Wonjoon Kim
HLHakyeon Lee
Hakyeon Lee
JJJeonghwan Jeon
Jeonghwan Jeon
With the emergence of smartphones, the paradigm of the mobile ecosystem has changed rapidly. In particular, global mobile firms focus on technological competition when jostling for market position in recognition of the strategic need to secure a strong mobile platform. In this paper, we analyse the network structure of technological knowledge flows in mobile ecosystems using US patent citation information. We find that two subsectors, platform providers and application and software providers, are at the centre of knowledge exchange activity and play a brokerage role as the key knowledge mediator. Then, we categorise mobile firms into five different groups based on the patterns of their network centrality: knowledge keystone players, knowledge-distributing mediators, knowledge-absorbing mediators, catch-up players, and pure knowledge receivers. The categorisation of firms demonstrates that knowledge flows in the mobile industry converge towards a few leading firms, and such patterns are shaping the mobile ecosystem with respect to technological knowledge. The firms categorised as catch-up players have played a brokerage role within their group, while the firms categorised as knowledge keystone players play a brokerage role across different groups.

1 November 2019 in Technological Forecasting and Social Change
Verified
Journal Article
Closed access
YSYongyoon Suh
Yongyoon Suh
CWChulwan Woo
Chulwan Woo
JKJinhwan Koh
Jinhwan Koh
JJJeonghwan Jeon
Jeonghwan Jeon

3 January 2013 in Technology Analysis & Strategic Management
Verified
Journal Article
Closed access
YGYoungjung Geum
Youngjung Geum
JJJeonghwan Jeon
Jeonghwan Jeon
HSHyeonju Seol
Hyeonju Seol
While identification of technological opportunities has received considerable attention, previous studies have some weaknesses in terms of subjectivity when finding the opportunities in practical terms. This paper proposes a systematic framework to identify technological opportunities, focusing on objective evidences which are specific and practical to be used in a business environment. To do this, we used patents as a source and employed a novelty detection technique whose primary object is detecting the novel pattern. To begin with, the patents are collected from the United States Patent and Trademark Office (USPTO) database. These patents are then pre-processed into a structured keyword vector that can represent the characteristics of each patent. These keyword vectors are then used to analyse the new and emerging pattern, using the novelty detection technique. As the final step, the results are analysed to identify the technological opportunities. A case study on laser technology in lithography is presented to show the proposed framework.