Jae Kwon Bae

845 total citations
34 papers, 581 citations indexed

About

Jae Kwon Bae is a scholar working on Artificial Intelligence, Information Systems and Accounting. According to data from OpenAlex, Jae Kwon Bae has authored 34 papers receiving a total of 581 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 10 papers in Information Systems and 9 papers in Accounting. Recurrent topics in Jae Kwon Bae's work include Financial Distress and Bankruptcy Prediction (9 papers), Imbalanced Data Classification Techniques (8 papers) and Technology Adoption and User Behaviour (7 papers). Jae Kwon Bae is often cited by papers focused on Financial Distress and Bankruptcy Prediction (9 papers), Imbalanced Data Classification Techniques (8 papers) and Technology Adoption and User Behaviour (7 papers). Jae Kwon Bae collaborates with scholars based in South Korea. Jae Kwon Bae's co-authors include Byeonghwa Park, Jin-Hwa Kim, Sungbin Cho, Daewon Kim, Dae‐Won Kim, Chulmo Koo, Namho Lee, Kyung Jin and Jungwoo Lee and has published in prestigious journals such as Expert Systems with Applications, Information Systems Frontiers and International Journal of Distributed Sensor Networks.

In The Last Decade

Jae Kwon Bae

30 papers receiving 528 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jae Kwon Bae South Korea 10 178 126 116 115 70 34 581
Douglas Kline United States 9 64 0.4× 54 0.4× 114 1.0× 111 1.0× 70 1.0× 26 631
Emmanuel Fragnière Switzerland 13 125 0.7× 54 0.4× 58 0.5× 32 0.3× 47 0.7× 91 493
G. Peter Zhang United States 14 72 0.4× 62 0.5× 129 1.1× 118 1.0× 41 0.6× 19 666
Yeong-Jia Goo Taiwan 10 111 0.6× 261 2.1× 119 1.0× 183 1.6× 39 0.6× 20 849
Germán G. Creamer United States 12 247 1.4× 40 0.3× 217 1.9× 143 1.2× 145 2.1× 51 655
Gholamreza Mansourfar Iran 11 205 1.2× 91 0.7× 381 3.3× 68 0.6× 179 2.6× 27 618
Prajwal Eachempati India 11 67 0.4× 21 0.2× 109 0.9× 68 0.6× 38 0.5× 30 467
Indranil Ghosh India 18 431 2.4× 36 0.3× 316 2.7× 91 0.8× 118 1.7× 63 842
Enric Junqué de Fortuny Belgium 10 66 0.4× 41 0.3× 112 1.0× 180 1.6× 27 0.4× 15 527
Zhiyuan Chen China 8 190 1.1× 39 0.3× 38 0.3× 198 1.7× 21 0.3× 26 738

Countries citing papers authored by Jae Kwon Bae

Since Specialization
Citations

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

Fields of papers citing papers by Jae Kwon Bae

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jae Kwon Bae. 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 Jae Kwon Bae. The network helps show where Jae Kwon Bae may publish in the future.

Co-authorship network of co-authors of Jae Kwon Bae

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

All Works

20 of 20 papers shown
1.
Bae, Jae Kwon, et al.. (2023). A Study on Digital Financial Security Threats and Cybersecurity Policies. 20(6). 133–151. 2 indexed citations
2.
Bae, Jae Kwon, et al.. (2022). A Study on the Innovation Resistance and Innovation Acceptance on Smart Factory: Focused on Smelting Industry. The e-Business Studies. 23(6). 235–252. 1 indexed citations
3.
Kim, Dae‐Won & Jae Kwon Bae. (2020). The Effects of Protection Motivation and Perceived Innovation Characteristics on Innovation Resistance and Innovation Acceptance in Internet Primary Bank Services. GLOBAL BUSINESS & FINANCE REVIEW. 25(1). 1–12. 9 indexed citations
4.
Bae, Jae Kwon, et al.. (2019). A comparative study on the effect of cultural dimensions on social capital and life satisfaction between filipinos and Korean facebook users. Journal of Theoretical and Applied Information Technology. 97(4). 1246–1261. 1 indexed citations
5.
Bae, Jae Kwon, et al.. (2019). Artificial Intelligence Techniques for Predicting Online Peer-to-Peer(P2P) Loan Default. The e-Business Studies. 23(3). 207–224. 1 indexed citations
6.
Bae, Jae Kwon. (2018). A Study on the Legal, Institutional, and Technical Factors for Activation of Domestic FinTech Industry: Focusing on the Consensus Delphi Technique. Asia-pacific Journal of Multimedia services convergent with Art, Humanities, and Sociology. 8(1). 101–112. 1 indexed citations
7.
8.
Bae, Jae Kwon. (2015). The Effects of Technological, Organizational and People Characteristics on Absorptive Capacity and Innovation Performance in IT Industrial Clusters. International Journal of Multimedia and Ubiquitous Engineering. 10(2). 383–394. 1 indexed citations
9.
10.
Kim, Jin-Hwa, et al.. (2012). Using genetic algorithm based knowledge refinement model for dividend policy forecasting. Expert Systems with Applications. 39(18). 13472–13479. 16 indexed citations
11.
Bae, Jae Kwon, et al.. (2011). An Empirical Study Applying the Self-Determination Factors to Acceptance of Microblogging Service. Korean Journal of Business Administration. 24(5). 2745–2774. 1 indexed citations
12.
Lee, Namho, Jae Kwon Bae, & Chulmo Koo. (2011). A case-based reasoning based multi-agent cognitive map inference mechanism: An application to sales opportunity assessment. Information Systems Frontiers. 14(3). 653–668. 9 indexed citations
13.
Bae, Jae Kwon & Jin-Hwa Kim. (2010). Combining models from neural networks and inductive learning algorithms. Expert Systems with Applications. 38(5). 4839–4850. 10 indexed citations
14.
Bae, Jae Kwon. (2010). Forecasting Decisions on Dividend Policy of South Korea Companies Listed in the Korea Exchange Market Based on Support Vector Machines. Journal of Convergence Information Technology. 5(8). 186–194. 8 indexed citations
15.
Kim, Jin-Hwa, et al.. (2009). A knowledge integration model for the prediction of corporate dividends. Expert Systems with Applications. 37(2). 1344–1350. 8 indexed citations
16.
Bae, Jae Kwon & Jin-Hwa Kim. (2009). Integration of heterogeneous models to predict consumer behavior. Expert Systems with Applications. 37(3). 1821–1826. 23 indexed citations
17.
Kim, Jin-Hwa, et al.. (2008). A Corporate Dividend Policy UJsing Human Knowledge Process Model. 7. 479–486.
18.
Cho, Sungbin, Jin-Hwa Kim, & Jae Kwon Bae. (2007). An integrative model with subject weight based on neural network learning for bankruptcy prediction. Expert Systems with Applications. 36(1). 403–410. 59 indexed citations
19.
Kim, Jin-Hwa, et al.. (2007). Prediction of Personal Credit Rates with Incomplete Data Sets Using Cognitive Mapping. 2007 International Conference on Convergence Information Technology (ICCIT 2007). 25. 1912–1917. 2 indexed citations
20.
Bae, Jae Kwon, Jin-Hwa Kim, & Jungwoo Lee. (2007). Integration of Heterogeneous Models with Knowledge Consolidation. 2007 International Conference on Convergence Information Technology (ICCIT 2007). 22. 1510–1516. 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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2026