Kaoru Tone

25.8k total citations · 13 hit papers
90 papers, 19.8k citations indexed

About

Kaoru Tone is a scholar working on Management Science and Operations Research, Economics and Econometrics and Finance. According to data from OpenAlex, Kaoru Tone has authored 90 papers receiving a total of 19.8k indexed citations (citations by other indexed papers that have themselves been cited), including 71 papers in Management Science and Operations Research, 47 papers in Economics and Econometrics and 13 papers in Finance. Recurrent topics in Kaoru Tone's work include Efficiency Analysis Using DEA (67 papers), Economic and Environmental Valuation (13 papers) and Economic Growth and Productivity (13 papers). Kaoru Tone is often cited by papers focused on Efficiency Analysis Using DEA (67 papers), Economic and Environmental Valuation (13 papers) and Economic Growth and Productivity (13 papers). Kaoru Tone collaborates with scholars based in Japan, United States and United Kingdom. Kaoru Tone's co-authors include W. W. Cooper, Lawrence M. Seiford, Miki Tsutsui, Biresh K. Sahoo, Joe Zhu, Wade D. Cook, L. Seiford, Mehdi Toloo, Mohammad Izadikhah and Tsung‐Sheng Chang and has published in prestigious journals such as European Journal of Operational Research, Expert Systems with Applications and International Journal of Production Economics.

In The Last Decade

Kaoru Tone

85 papers receiving 18.9k citations

Hit Papers

A slacks-based measure of efficiency in data envelopment ... 1999 2026 2008 2017 2001 1999 2002 2007 2008 1000 2.0k 3.0k 4.0k
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.

  1. A slacks-based measure of efficiency in data envelopment analysis
    2001 4.2k citations Kaoru Tone European Journal of Operational Research profile →
  2. Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software
    1999 3.0k citations W. W. Cooper, Lawrence M. Seiford et al. profile →
  3. A slacks-based measure of super-efficiency in data envelopment analysis
    2002 2.8k citations Kaoru Tone European Journal of Operational Research profile →
  4. Data Envelopment Analysis
    2007 1.4k citations W. W. Cooper, Lawrence M. Seiford et al. profile →
  5. Network DEA: A slacks-based measure approach
    2008 961 citations Kaoru Tone, Miki Tsutsui European Journal of Operational Research profile →
  6. Data Envelopment Analysis
    2000 835 citations W. W. Cooper, Lawrence M. Seiford et al. profile →
  7. Dynamic DEA: A slacks-based measure approach☆
    2009 763 citations Kaoru Tone, Miki Tsutsui Omega profile →
  8. Data envelopment analysis: Prior to choosing a model
    2013 606 citations Kaoru Tone et al. Omega profile →
  9. Introduction to Data Envelopment Analysis and Its Uses: With DEA-Solver Software and References
    2005 597 citations Lawrence M. Seiford, W. W. Cooper et al. Medical Entomology and Zoology profile →
  10. Dynamic DEA with network structure: A slacks-based measure approach
    2013 563 citations Kaoru Tone, Miki Tsutsui Omega profile →
  11. Dealing with Undesirable Outputs in DEA: A Slacks-based Measure (SBM) Approach
    2003 522 citations Kaoru Tone Medical Entomology and Zoology profile →
  12. An epsilon-based measure of efficiency in DEA – A third pole of technical efficiency
    2010 503 citations Kaoru Tone, Miki Tsutsui European Journal of Operational Research profile →
  13. Introduction to Data Envelopment Analysis and Its Uses
    2006 473 citations W. W. Cooper, Lawrence M. Seiford et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kaoru Tone Japan 37 13.2k 11.0k 4.0k 2.2k 1.8k 90 19.8k
Rolf Färe United States 64 15.5k 1.2× 14.7k 1.3× 4.9k 1.2× 2.0k 0.9× 2.2k 1.3× 316 22.7k
Shawna Grosskopf United States 68 14.7k 1.1× 14.5k 1.3× 5.0k 1.2× 2.0k 0.9× 2.2k 1.3× 257 22.0k
Joe Zhu United States 72 14.9k 1.1× 7.9k 0.7× 2.1k 0.5× 2.8k 1.3× 1.8k 1.0× 208 18.9k
Lawrence M. Seiford United States 36 12.9k 1.0× 7.4k 0.7× 1.6k 0.4× 2.0k 0.9× 1.9k 1.1× 70 16.7k
Edwardo L. Rhodes United States 5 18.0k 1.4× 10.9k 1.0× 2.3k 0.6× 3.1k 1.4× 2.6k 1.5× 9 24.5k
C. A. Knox Lovell United States 49 18.1k 1.4× 16.0k 1.5× 2.7k 0.7× 2.8k 1.3× 4.2k 2.4× 150 27.5k
George E. Battese Australia 28 13.8k 1.0× 11.7k 1.1× 1.4k 0.4× 2.7k 1.2× 2.4k 1.3× 66 22.9k
Wade D. Cook Canada 54 9.6k 0.7× 4.4k 0.4× 1.1k 0.3× 1.4k 0.6× 793 0.4× 195 11.9k
Toshiyuki Sueyoshi United States 61 6.1k 0.5× 5.1k 0.5× 3.1k 0.8× 1.5k 0.7× 439 0.2× 201 9.5k
Ali Emrouznejad United Kingdom 52 6.7k 0.5× 3.6k 0.3× 1.0k 0.3× 1.2k 0.6× 984 0.6× 245 9.9k

Countries citing papers authored by Kaoru Tone

Since Specialization
Citations

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

Fields of papers citing papers by Kaoru Tone

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kaoru Tone

This figure shows the co-authorship network connecting the top 25 collaborators of Kaoru Tone. A scholar is included among the top collaborators of Kaoru Tone 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 Kaoru Tone. Kaoru Tone 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.
Kweh, Qian Long, et al.. (2024). Evaluating the resource management and profitability efficiencies of US commercial banks from a dynamic network perspective. Financial Innovation. 10(1). 7 indexed citations
2.
Chang, Tsung‐Sheng, et al.. (2020). Nested dynamic network data envelopment analysis models with infinitely many decision making units for portfolio evaluation. European Journal of Operational Research. 291(2). 766–781. 21 indexed citations
3.
Tone, Kaoru, Qian Long Kweh, Wen‐Min Lu, & Irene Wei Kiong Ting. (2018). Modeling investments in the dynamic network performance of insurance companies. Omega. 88. 237–247. 54 indexed citations
4.
5.
Johnes, Geraint & Kaoru Tone. (2016). The efficiency of higher education institutions in England revisited: comparing alternative measures. Tertiary Education and Management. 23(3). 191–205. 34 indexed citations
6.
Ouenniche, Jamal, Bing Xu, & Kaoru Tone. (2014). Relative Performance Evaluation of Competing Crude Oil Prices’ Volatility Forecasting Models: A Slacks-Based Super-Efficiency DEA Model. American Journal of Operations Research. 4(4). 235–245. 6 indexed citations
7.
Tsutsui, Miki & Kaoru Tone. (2013). 2-D-8 Malmquist Index in the Dynamic & Network DEA model. 2013. 206–207. 3 indexed citations
8.
Sahoo, Biresh K., Kristiaan Kerstens, & Kaoru Tone. (2012). Returns to growth in a non parametric DEA approach. RePEc: Research Papers in Economics.
9.
Sahoo, Biresh K., et al.. (2009). Fair Allocation Based on Two Criteria: A DEA Game View of Add Them Up and Divide by Two. SSRN Electronic Journal. 1 indexed citations
10.
Tone, Kaoru & Miki Tsutsui. (2009). TUNING REGRESSION RESULTS FOR USE IN MULTI-STAGE DATA ADJUSTMENT APPROACH OF DEA(<Special Issue>Operations Research for Performance Evaluation). Journal of the Operations Research Society of Japan. 52(2). 76–85. 3 indexed citations
11.
Sahoo, Biresh K. & Kaoru Tone. (2008). Radial and non-radial decompositions of profit change: With an application to Indian banking. European Journal of Operational Research. 196(3). 1130–1146. 50 indexed citations
12.
Liu, Junming & Kaoru Tone. (2006). A multistage method to measure efficiency and its application to Japanese banking industry. Socio-Economic Planning Sciences. 42(2). 75–91. 61 indexed citations
13.
Tone, Kaoru. (2002). A slacks-based measure of super-efficiency in data envelopment analysis. European Journal of Operational Research. 143(1). 32–41. 2805 indexed citations breakdown →
14.
Tone, Kaoru. (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research. 130(3). 498–509. 4194 indexed citations breakdown →
15.
Cooper, W. W., Lawrence M. Seiford, & Kaoru Tone. (2000). Data Envelopment Analysis. 835 indexed citations breakdown →
16.
Tone, Kaoru. (1994). A note on group vs. individual decision making in AHP. Medical Entomology and Zoology. 2 indexed citations
17.
Tone, Kaoru. (1983). Revisions of constraint approximations in the successive QP method for nonlinear programming problems. Mathematical Programming. 26(2). 144–152. 40 indexed citations
18.
Tone, Kaoru. (1977). ON OPTIMAL PATTERN FLOWS. Journal of the Operations Research Society of Japan. 20(2). 77–93.
19.
Tone, Kaoru. (1976). A parametrization of Benders decomposition. 29(5). 53–60. 1 indexed citations
20.
Tone, Kaoru. (1976). Postoptimization of quadratic programming. 29(1). 1–6. 2 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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