Jing–Rong Chang

1.3k total citations
32 papers, 962 citations indexed

About

Jing–Rong Chang is a scholar working on Management Science and Operations Research, Artificial Intelligence and Economics and Econometrics. According to data from OpenAlex, Jing–Rong Chang has authored 32 papers receiving a total of 962 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Management Science and Operations Research, 14 papers in Artificial Intelligence and 6 papers in Economics and Econometrics. Recurrent topics in Jing–Rong Chang's work include Multi-Criteria Decision Making (12 papers), Stock Market Forecasting Methods (6 papers) and Fuzzy Logic and Control Systems (6 papers). Jing–Rong Chang is often cited by papers focused on Multi-Criteria Decision Making (12 papers), Stock Market Forecasting Methods (6 papers) and Fuzzy Logic and Control Systems (6 papers). Jing–Rong Chang collaborates with scholars based in Taiwan. Jing–Rong Chang's co-authors include Ching‐Hsue Cheng, Ming‐Hung Shu, Long‐Sheng Chen, Liang‐Ying Wei, Kuei‐Hu Chang, Shu-Hsien Liao, Ying-Chieh Tsai, Mu‐Yen Chen, Pei‐Yu Yu and Chia‐Wei Chang and has published in prestigious journals such as Expert Systems with Applications, IEEE Access and Technological Forecasting and Social Change.

In The Last Decade

Jing–Rong Chang

31 papers receiving 912 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jing–Rong Chang Taiwan 13 629 237 210 180 126 32 962
Xiaomei Mi China 17 887 1.4× 291 1.2× 215 1.0× 108 0.6× 81 0.6× 24 1.2k
Hong‐gang Peng China 18 902 1.4× 338 1.4× 262 1.2× 179 1.0× 72 0.6× 26 1.2k
Ludmila Dymova Poland 18 817 1.3× 303 1.3× 255 1.2× 282 1.6× 41 0.3× 39 1.1k
Liguo Fei China 25 888 1.4× 582 2.5× 273 1.3× 203 1.1× 197 1.6× 57 1.7k
Chih‐Chou Chiu Taiwan 13 537 0.9× 454 1.9× 159 0.8× 61 0.3× 136 1.1× 21 1.2k
Christophe Labreuche France 15 876 1.4× 434 1.8× 230 1.1× 353 2.0× 38 0.3× 61 1.3k
Pavel Sevastjanov Poland 18 772 1.2× 289 1.2× 259 1.2× 288 1.6× 47 0.4× 37 1.0k
Waldemar W. Koczkodaj Canada 19 659 1.0× 263 1.1× 92 0.4× 127 0.7× 33 0.3× 65 1.0k
Ru‐xin Nie China 19 811 1.3× 340 1.4× 166 0.8× 79 0.4× 110 0.9× 35 1.2k
Roberta Parreiras Brazil 14 595 0.9× 299 1.3× 160 0.8× 58 0.3× 37 0.3× 24 870

Countries citing papers authored by Jing–Rong Chang

Since Specialization
Citations

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

Fields of papers citing papers by Jing–Rong Chang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jing–Rong Chang. 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 Jing–Rong Chang. The network helps show where Jing–Rong Chang may publish in the future.

Co-authorship network of co-authors of Jing–Rong Chang

This figure shows the co-authorship network connecting the top 25 collaborators of Jing–Rong Chang. A scholar is included among the top collaborators of Jing–Rong Chang 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 Jing–Rong Chang. Jing–Rong Chang 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.
Chang, Jing–Rong, et al.. (2024). Using feature selection methods to identify important factors of in-game consumption. Enterprise Information Systems. 19(3-4). 1 indexed citations
2.
Chang, Jing–Rong, et al.. (2023). Building prediction models and discovering important factors of health insurance fraud using machine learning methods. Journal of Ambient Intelligence and Humanized Computing. 14(7). 9607–9619. 11 indexed citations
3.
Chen, Wen-Kuo, et al.. (2022). Using refined kano model and decision trees to discover learners’ needs for teaching videos. Multimedia Tools and Applications. 81(6). 8317–8347. 10 indexed citations
4.
Cheng, Ching‐Hsue, Mu‐Yen Chen, & Jing–Rong Chang. (2022). Linguistic multi-criteria decision-making aggregation model based on situational ME-LOWA and ME-LOWGA operators. Granular Computing. 8(1). 97–110. 5 indexed citations
5.
Chang, Jing–Rong, et al.. (2022). Winning customers' hearts and minds using DFSS in the insurance industry. The TQM Journal. 38(1). 95–112. 3 indexed citations
6.
Chen, Mu‐Yen, et al.. (2022). Identifying the key success factors of movie projects in crowdfunding. Multimedia Tools and Applications. 81(19). 27711–27736. 8 indexed citations
7.
Chang, Jing–Rong, Long‐Sheng Chen, & Chia‐Wei Chang. (2020). New term weighting methods for classifying textual sentiment data. 17(3). 257–268. 1 indexed citations
8.
Cheng, Ching‐Hsue, et al.. (2020). A novel weighted distance threshold method for handling medical missing values. Computers in Biology and Medicine. 122. 103824–103824. 18 indexed citations
9.
Chang, Jing–Rong, et al.. (2020). Novel feature selection approaches for improving the performance of sentiment classification. Journal of Ambient Intelligence and Humanized Computing. 24 indexed citations
10.
Chang, Jing–Rong, et al.. (2019). Why Customers Don’t Revisit in Tourism and Hospitality Industry?. IEEE Access. 7. 146588–146606. 34 indexed citations
11.
Chen, Long‐Sheng, et al.. (2016). A Decision Tree Based Method for Extracting Important Elements of In-Applications Purchase. 3. 138–141. 2 indexed citations
12.
Chen, Long‐Sheng, et al.. (2015). FIR: An Effective Scheme for Extracting Useful Metadata from Social Media. Journal of Medical Systems. 39(11). 139–139. 8 indexed citations
13.
Chang, Jing–Rong & Yujie Huang. (2011). A weighted fuzzy time series model based on adoptive OWA operators. 4570. 94–97. 4 indexed citations
14.
Cheng, Ching‐Hsue, et al.. (2006). Extracting drug utilization knowledge using self-organizing map and rough set theory. Expert Systems with Applications. 33(2). 499–508. 35 indexed citations
15.
Chang, Jing–Rong, et al.. (2006). Selecting Weapon System Using Relative Distance Metric Method. Soft Computing. 11(6). 573–584. 8 indexed citations
16.
Tsai, Ying-Chieh, Ching‐Hsue Cheng, & Jing–Rong Chang. (2005). Entropy-based fuzzy rough classification approach for extracting classification rules. Expert Systems with Applications. 31(2). 436–443. 38 indexed citations
17.
Chang, Jing–Rong, Kuei‐Hu Chang, Shu-Hsien Liao, & Ching‐Hsue Cheng. (2005). The reliability of general vague fault-tree analysis on weapon systems fault diagnosis. Soft Computing. 10(7). 531–542. 64 indexed citations
18.
Chang, Jing–Rong, et al.. (2005). Dynamic fuzzy OWA model for group multiple criteria decision making. Soft Computing. 10(7). 543–554. 23 indexed citations
19.
Cheng, Ching‐Hsue, et al.. (2005). Entropy-based and trapezoid fuzzification-based fuzzy time series approaches for forecasting IT project cost. Technological Forecasting and Social Change. 73(5). 524–542. 125 indexed citations
20.
Chang, Yau‐Zen, Jing–Rong Chang, & Chun-Kai Huang. (2003). Parallel genetic algorithms for a neurocontrol problem. 6. 4151–4155. 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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