Seung‐pyo Jun

1.2k total citations · 1 hit paper
32 papers, 825 citations indexed

About

Seung‐pyo Jun is a scholar working on Economics and Econometrics, Management Science and Operations Research and Sociology and Political Science. According to data from OpenAlex, Seung‐pyo Jun has authored 32 papers receiving a total of 825 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Economics and Econometrics, 9 papers in Management Science and Operations Research and 8 papers in Sociology and Political Science. Recurrent topics in Seung‐pyo Jun's work include Innovation Diffusion and Forecasting (9 papers), Innovation Policy and R&D (8 papers) and Consumer Market Behavior and Pricing (5 papers). Seung‐pyo Jun is often cited by papers focused on Innovation Diffusion and Forecasting (9 papers), Innovation Policy and R&D (8 papers) and Consumer Market Behavior and Pricing (5 papers). Seung‐pyo Jun collaborates with scholars based in South Korea and Singapore. Seung‐pyo Jun's co-authors include Hyoung Sun Yoo, Do-Hyung Park, Jae‐Seong Lee, Hyunwoo Park, Juyeon Lee, Chul Lee, Ye Lim Jung, JeeNa Hwang, Sang‐Gook Kim and Soohyun Park and has published in prestigious journals such as Scientific Reports, Energy Policy and Technological Forecasting and Social Change.

In The Last Decade

Seung‐pyo Jun

29 papers receiving 779 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Ten years of research change using Google Trends: From the perspective of big data utilizations and applications

2017 329 citations Seung‐pyo Jun, Hyoung Sun Yoo et al. Technological Forecasting and Social Change profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Seung‐pyo Jun South Korea	14	272	177	168	125	114	32	825
Claudimar Pereira da Veiga Brazil	18	146 0.5×	42 0.2×	201 1.2×	101 0.8×	117 1.0×	170	1.1k
Jana Suklan United Kingdom	12	154 0.6×	41 0.2×	74 0.4×	65 0.5×	52 0.5×	41	867
Mark Elliot United Kingdom	20	357 1.3×	91 0.5×	107 0.6×	203 1.6×	87 0.8×	92	1.1k
Jonathan Cave United Kingdom	16	190 0.7×	44 0.2×	152 0.9×	85 0.7×	30 0.3×	86	1.0k
Amanda M. Y. Chu Hong Kong	16	116 0.4×	45 0.3×	325 1.9×	37 0.3×	43 0.4×	63	911
Alexander D’Amour United States	11	113 0.4×	86 0.5×	303 1.8×	83 0.7×	12 0.1×	21	1.0k
Ciara Heavin Ireland	15	131 0.5×	23 0.1×	95 0.6×	59 0.5×	36 0.3×	92	917
Giuliano Resce Italy	15	101 0.4×	41 0.2×	143 0.9×	101 0.8×	34 0.3×	54	606
Song Yao United States	15	182 0.7×	17 0.1×	208 1.2×	101 0.8×	383 3.4×	47	758
İrem Önder Austria	22	1.1k 3.9×	43 0.2×	157 0.9×	113 0.9×	568 5.0×	37	1.6k

Countries citing papers authored by Seung‐pyo Jun

Since Specialization

Citations

This map shows the geographic impact of Seung‐pyo Jun's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Seung‐pyo Jun with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Seung‐pyo Jun more than expected).

Fields of papers citing papers by Seung‐pyo Jun

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Seung‐pyo Jun. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Seung‐pyo Jun. The network helps show where Seung‐pyo Jun may publish in the future.

Co-authorship network of co-authors of Seung‐pyo Jun

This figure shows the co-authorship network connecting the top 25 collaborators of Seung‐pyo Jun. A scholar is included among the top collaborators of Seung‐pyo Jun based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Seung‐pyo Jun. Seung‐pyo Jun is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Yoo, Hyoung Sun, Ye Lim Jung, & Seung‐pyo Jun. (2023). The effects of SMEs' R&D team diversity on project‐level performances: evidence from South Korea's R&D subsidy program. R and D Management. 53(3). 391–407. 9 indexed citations

Yoo, Hyoung Sun, Ye Lim Jung, & Seung‐pyo Jun. (2023). Prediction of SMEs’ R&D performances by machine learning for project selection. Scientific Reports. 13(1). 7598–7598. 7 indexed citations

Jun, Seung‐pyo, Hyoung Sun Yoo, & Chul Lee. (2021). Young people are not blameworthy: the generation’s awareness of COVID-19 and behavioral responses. Scientific Reports. 11(1). 23595–23595. 2 indexed citations

Jun, Seung‐pyo, Hyoung Sun Yoo, & Jae‐Seong Lee. (2021). The impact of the pandemic declaration on public awareness and behavior: Focusing on COVID-19 google searches. Technological Forecasting and Social Change. 166. 120592–120592. 40 indexed citations

Lee, Jae‐Seong & Seung‐pyo Jun. (2020). Privacy-preserving data mining for open government data from heterogeneous sources. Government Information Quarterly. 38(1). 101544–101544. 26 indexed citations

Jun, Seung‐pyo, et al.. (2020). Method of improving the performance of public-private innovation networks by linking heterogeneous DBs: Prediction using ensemble and PPDM models. Technological Forecasting and Social Change. 161. 120258–120258. 19 indexed citations

Jun, Seung‐pyo, et al.. (2019). A Study on Efficiency of Collaborative Research Using PPDM-based Heterogeneous DB Linkage. Journal of Korea Technology Innovation Society. 22(4). 548–575. 1 indexed citations

Jun, Seung‐pyo, et al.. (2018). A Study on Automatic Classification Model of Documents Based on Korean Standard Industrial Classification. Journal of Intelligence and Information Systems. 24(3). 221–241. 1 indexed citations

Yoo, Hyoung Sun, Chul Lee, & Seung‐pyo Jun. (2018). The Characteristics of SMEs Preferring Cooperative Research and Development Support from the Government: The Case of Korea. Sustainability. 10(9). 3048–3048. 11 indexed citations

10.

Jun, Seung‐pyo, et al.. (2017). A Study of the Distinctive Characteristics of Government Funded Research Institutes Engaged in Technological Cooperation with SMEs. Journal of Korea Technology Innovation Society. 20(3). 607–641. 2 indexed citations

11.

Jun, Seung‐pyo, et al.. (2017). A Study on Web-based Technology Valuation System. Journal of Intelligence and Information Systems. 23(1). 23–46. 2 indexed citations

12.

Jun, Seung‐pyo, et al.. (2017). A Data-based Sales Forecasting Support System for New Businesses. Journal of Intelligence and Information Systems. 23(1). 1–22. 2 indexed citations

13.

Jun, Seung‐pyo & Do-Hyung Park. (2017). Visualization of brand positioning based on consumer web search information. Internet Research. 27(2). 381–407. 19 indexed citations

14.

Jun, Seung‐pyo, et al.. (2016). A study on the effects of the CAFE standard on consumers. Energy Policy. 91. 148–160. 17 indexed citations

15.

Jun, Seung‐pyo, Sang‐Gook Kim, & Hyunwoo Park. (2016). The mismatch between demand and beneficiaries of R&D support programs for SMEs: Evidence from Korean R&D planning programs. Technological Forecasting and Social Change. 116. 286–298. 7 indexed citations

16.

Yoo, Hyoung Sun, et al.. (2015). A Study on an Estimation Method of Domestic Market Size by Using the Standard Statistical Classifications. Journal of Korea Technology Innovation Society. 18(3). 387–415.

17.

Yoo, Hyoung Sun, et al.. (2013). Study on the Selection Method of the Focused Supporting Industries for the Maximization of SMEs’ Technological Innovation. Journal of Korea Technology Innovation Society. 16(1). 41–62. 1 indexed citations

18.

Jun, Seung‐pyo, et al.. (2013). A Comparative Study of Consumer’s Hype Cycles Using Web Search Traffic of Naver and Google. Journal of Korea Technology Innovation Society. 16(4). 1109–1133. 1 indexed citations

19.

Jun, Seung‐pyo, et al.. (2013). A study of the method using search traffic to analyze new technology adoption. Technological Forecasting and Social Change. 81. 82–95. 51 indexed citations

20.

Jun, Seung‐pyo, et al.. (2012). The Development of the Method of Determining Remaining Cited-patent Life Time Using the Survival Curve Analysis. Journal of Korea Technology Innovation Society. 15(4). 745–765. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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