Juram Kim

516 total citations
13 papers, 379 citations indexed

About

Juram Kim is a scholar working on Management of Technology and Innovation, Strategy and Management and Management Science and Operations Research. According to data from OpenAlex, Juram Kim has authored 13 papers receiving a total of 379 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Management of Technology and Innovation, 3 papers in Strategy and Management and 3 papers in Management Science and Operations Research. Recurrent topics in Juram Kim's work include Intellectual Property and Patents (8 papers), Machine Learning in Materials Science (3 papers) and Innovation Diffusion and Forecasting (3 papers). Juram Kim is often cited by papers focused on Intellectual Property and Patents (8 papers), Machine Learning in Materials Science (3 papers) and Innovation Diffusion and Forecasting (3 papers). Juram Kim collaborates with scholars based in South Korea. Juram Kim's co-authors include Chang‐Yong Lee, Han‐Gyun Woo, Seungho Kim, Gyumin Lee, Chiehyeon Lim, Oh-Jin Kwon, Joon Mo Ahn, Young‐Choon Kim, Sungjoo Lee and Dong‐Gi Lee and has published in prestigious journals such as Expert Systems with Applications, Technological Forecasting and Social Change and Technovation.

In The Last Decade

Juram Kim

13 papers receiving 368 citations

Peers

Juram Kim
Limin Zhang China
Heeyong Noh South Korea
Wonchul Seo South Korea
Han‐Gyun Woo South Korea
Inchae Park South Korea
Byunghoon Kim South Korea
Reinhard Haupt Germany
Oh-Jin Kwon South Korea
Lothar Walter Germany
Shu Fang China
Limin Zhang China
Juram Kim
Citations per year, relative to Juram Kim Juram Kim (= 1×) peers Limin Zhang

Countries citing papers authored by Juram Kim

Since Specialization
Citations

This map shows the geographic impact of Juram Kim'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 Juram Kim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Juram Kim more than expected).

Fields of papers citing papers by Juram Kim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Juram Kim. 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 Juram Kim. The network helps show where Juram Kim may publish in the future.

Co-authorship network of co-authors of Juram Kim

This figure shows the co-authorship network connecting the top 25 collaborators of Juram Kim. A scholar is included among the top collaborators of Juram Kim 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 Juram Kim. Juram Kim is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

13 of 13 papers shown
1.
Lee, Dong‐Gi, et al.. (2024). Dissatisfaction-considered waiting time prediction for outpatients with interpretable machine learning. Health Care Management Science. 27(3). 370–390. 3 indexed citations
2.
3.
Kim, Juram, et al.. (2022). Towards expert–machine collaborations for technology valuation: An interpretable machine learning approach. Technological Forecasting and Social Change. 183. 121940–121940. 24 indexed citations
4.
Lee, Gyumin, Juram Kim, & Chang‐Yong Lee. (2022). State-of-health estimation of Li-ion batteries in the early phases of qualification tests: An interpretable machine learning approach. Expert Systems with Applications. 197. 116817–116817. 51 indexed citations
5.
Kim, Juram, et al.. (2021). Screening ideas in the early stages of technology development: A word2vec and convolutional neural network approach. Technovation. 112. 102407–102407. 27 indexed citations
6.
Lee, Chang‐Yong, et al.. (2021). Anticipating multi-technology convergence: a machine learning approach using patent information. Scientometrics. 126(3). 1867–1896. 34 indexed citations
7.
Kim, Juram & Chiehyeon Lim. (2021). Customer complaints monitoring with customer review data analytics: An integrated method of sentiment and statistical process control analyses. Advanced Engineering Informatics. 49. 101304–101304. 30 indexed citations
8.
Ahn, Joon Mo, et al.. (2021). A doc2vec and local outlier factor approach to measuring the novelty of patents. Technological Forecasting and Social Change. 174. 121294–121294. 29 indexed citations
9.
Kim, Juram, Seungho Kim, & Chang‐Yong Lee. (2018). Anticipating technological convergence: Link prediction using Wikipedia hyperlinks. Technovation. 79. 25–34. 74 indexed citations
10.
Kim, Juram & Chang‐Yong Lee. (2017). Stochastic service life cycle analysis using customer reviews. Service Industries Journal. 37(5-6). 296–316. 4 indexed citations
11.
Lee, Chang‐Yong, Juram Kim, Oh-Jin Kwon, & Han‐Gyun Woo. (2016). Stochastic technology life cycle analysis using multiple patent indicators. Technological Forecasting and Social Change. 106. 53–64. 64 indexed citations
12.
Lee, Chang‐Yong, et al.. (2016). Patterns of technology life cycles: stochastic analysis based on patent citations. Technology Analysis and Strategic Management. 29(1). 53–67. 23 indexed citations
13.
Lee, Chang‐Yong, Juram Kim, & Sungjoo Lee. (2016). Towards robust technology roadmapping: How to diagnose the vulnerability of organisational plans. Technological Forecasting and Social Change. 111. 164–175. 8 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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2026