IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v309y2022i2d10.1007_s10479-020-03668-8.html
   My bibliography  Save this article

DEA under big data: data enabled analytics and network data envelopment analysis

Author

Listed:
  • Joe Zhu

    (Worcester Polytechnic Institute)

Abstract

This paper proposes that data envelopment analysis (DEA) should be viewed as a method (or tool) for data-oriented analytics in performance evaluation and benchmarking. While computational algorithms have been developed to deal with large volumes of data (decision making units, inputs, and outputs) under the conventional DEA, valuable information hidden in big data that are represented by network structures should be extracted by DEA. These network structures, e.g., transportation and logistics systems, encompass a broader range of inter-linked metrics that cannot be modelled by conventional DEA. It is proposed that network DEA is related to the value dimension of big data. It is shown that network DEA is different from standard DEA, although it bears the name of DEA and some similarity with conventional DEA. Network DEA is big data enabled analytics (big DEA) when multiple (performance) metrics or attributes are linked through network structures. These network structures are too large or complex to be dealt with by conventional DEA. Unlike conventional DEA that are solved via linear programming, general network DEA corresponds to nonconvex optimization problems. This represents opportunities for developing techniques for solving non-linear network DEA models. Areas such as transportation and logistics system as well as supply chains have a great potential to use network DEA in big data modeling.

Suggested Citation

  • Joe Zhu, 2022. "DEA under big data: data enabled analytics and network data envelopment analysis," Annals of Operations Research, Springer, vol. 309(2), pages 761-783, February.
    • Handle: RePEc:spr:annopr:v:309:y:2022:i:2:d:10.1007_s10479-020-03668-8
    DOI: 10.1007/s10479-020-03668-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-020-03668-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-020-03668-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kao, Chiang, 2009. "Efficiency decomposition in network data envelopment analysis: A relational model," European Journal of Operational Research, Elsevier, vol. 192(3), pages 949-962, February.
    2. Guo, Chuanyin & Wei, Fajie & Chen, Yao, 2017. "A note on second order cone programming approach to two-stage network data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 263(2), pages 733-735.
    3. Rolf Färe & Shawna Grosskopf & Gerald Whittaker, 2014. "Network DEA II," International Series in Operations Research & Management Science, in: Wade D. Cook & Joe Zhu (ed.), Data Envelopment Analysis, edition 127, chapter 0, pages 307-327, Springer.
    4. Yong-Hong Kuo & Andrew Kusiak, 2019. "From data to big data in production research: the past and future trends," International Journal of Production Research, Taylor & Francis Journals, vol. 57(15-16), pages 4828-4853, August.
    5. Khezrimotlagh, Dariush & Zhu, Joe & Cook, Wade D. & Toloo, Mehdi, 2019. "Data envelopment analysis and big data," European Journal of Operational Research, Elsevier, vol. 274(3), pages 1047-1054.
    6. Shwartz, Michael & Burgess, James F. & Zhu, Joe, 2016. "A DEA based composite measure of quality and its associated data uncertainty interval for health care provider profiling and pay-for-performance," European Journal of Operational Research, Elsevier, vol. 253(2), pages 489-502.
    7. Li, Ye & Wang, Yan-zhang & Cui, Qiang, 2015. "Evaluating airline efficiency: An application of Virtual Frontier Network SBM," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 81(C), pages 1-17.
    8. Liang, Liang & Cook, Wade D. & Zhu, Joe, 2016. "DEA models for non-homogeneous DMUs with different input configurationsAuthor-Name: Li, WangHong," European Journal of Operational Research, Elsevier, vol. 254(3), pages 946-956.
    9. Tavassoli, Mohammad & Faramarzi, Gholam Reza & Farzipoor Saen, Reza, 2014. "Efficiency and effectiveness in airline performance using a SBM-NDEA model in the presence of shared input," Journal of Air Transport Management, Elsevier, vol. 34(C), pages 146-153.
    10. Azadi, Majid & Shabani, Amir & Khodakarami, Mohsen & Farzipoor Saen, Reza, 2015. "Reprint of “Planning in feasible region by two-stage target-setting DEA methods: An application in green supply chain management of public transportation service providers”," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 74(C), pages 22-36.
    11. Chen, Yao & Cook, Wade D. & Kao, Chiang & Zhu, Joe, 2013. "Network DEA pitfalls: Divisional efficiency and frontier projection under general network structures," European Journal of Operational Research, Elsevier, vol. 226(3), pages 507-515.
    12. Chen, Kun & Zhu, Joe, 2020. "Additive slacks-based measure: Computational strategy and extension to network DEA," Omega, Elsevier, vol. 91(C).
    13. Lahdelma, Risto & Salminen, Pekka, 2006. "Stochastic multicriteria acceptability analysis using the data envelopment model," European Journal of Operational Research, Elsevier, vol. 170(1), pages 241-252, April.
    14. Aparicio, Juan & Pastor, Jesús T. & Vidal, Fernando & Zofío, José L., 2017. "Evaluating productive performance: A new approach based on the product-mix problem consistent with Data Envelopment Analysis," Omega, Elsevier, vol. 67(C), pages 134-144.
    15. John S. Liu & Louis Y. Y. Lu & Wen-Min Lu, 2016. "Research Fronts and Prevailing Applications in Data Envelopment Analysis," International Series in Operations Research & Management Science, in: Joe Zhu (ed.), Data Envelopment Analysis, chapter 0, pages 543-574, Springer.
    16. Charles, Vincent & Aparicio, Juan & Zhu, Joe, 2019. "The curse of dimensionality of decision-making units: A simple approach to increase the discriminatory power of data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 279(3), pages 929-940.
    17. Wade D. Cook & Julie Harrison & Raha Imanirad & Paul Rouse & Joe Zhu, 2013. "Data Envelopment Analysis with Nonhomogeneous DMUs," Operations Research, INFORMS, vol. 61(3), pages 666-676, June.
    18. Chen, Po-Chi & Yu, Ming-Miin & Shih, Jou-Chen & Chang, Ching-Cheng & Hsu, Shih-Hsun, 2019. "A reassessment of the Global Food Security Index by using a hierarchical data envelopment analysis approach," European Journal of Operational Research, Elsevier, vol. 272(2), pages 687-698.
    19. Chen, Kun & Cook, Wade D. & Zhu, Joe, 2020. "A conic relaxation model for searching for the global optimum of network data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 280(1), pages 242-253.
    20. Chen, Yao & Cook, Wade D. & Li, Ning & Zhu, Joe, 2009. "Additive efficiency decomposition in two-stage DEA," European Journal of Operational Research, Elsevier, vol. 196(3), pages 1170-1176, August.
    21. Kao, Chiang, 2014. "Network data envelopment analysis: A review," European Journal of Operational Research, Elsevier, vol. 239(1), pages 1-16.
    22. Cook, Wade D. & Tone, Kaoru & Zhu, Joe, 2014. "Data envelopment analysis: Prior to choosing a model," Omega, Elsevier, vol. 44(C), pages 1-4.
    23. Dariush Khezrimotlagh & Yao Chen, 2018. "Data Envelopment Analysis," International Series in Operations Research & Management Science, in: Decision Making and Performance Evaluation Using Data Envelopment Analysis, chapter 0, pages 217-234, Springer.
    24. Chao, Shih-Liang & Yu, Ming-Miin & Hsieh, Wei-Fan, 2018. "Evaluating the efficiency of major container shipping companies: A framework of dynamic network DEA with shared inputs," Transportation Research Part A: Policy and Practice, Elsevier, vol. 117(C), pages 44-57.
    25. Kottas, Angelos T. & Madas, Michael A., 2018. "Comparative efficiency analysis of major international airlines using Data Envelopment Analysis: Exploring effects of alliance membership and other operational efficiency determinants," Journal of Air Transport Management, Elsevier, vol. 70(C), pages 1-17.
    26. Chen, Kun & Zhu, Joe, 2017. "Second order cone programming approach to two-stage network data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 262(1), pages 231-238.
    27. Shao, Yanmin & Sun, Changfu, 2016. "Performance evaluation of China's air routes based on network data envelopment analysis approach," Journal of Air Transport Management, Elsevier, vol. 55(C), pages 67-75.
    28. Olesen, Ole Bent & Petersen, Niels Christian & Podinovski, Victor V., 2015. "Efficiency analysis with ratio measures," European Journal of Operational Research, Elsevier, vol. 245(2), pages 446-462.
    29. Olfat, Laya & Amiri, Maghsoud & Bamdad Soufi, Jahanyar & Pishdar, Mahsa, 2016. "A dynamic network efficiency measurement of airports performance considering sustainable development concept: A fuzzy dynamic network-DEA approach," Journal of Air Transport Management, Elsevier, vol. 57(C), pages 272-290.
    30. Chiang Kao, 2014. "Efficiency Decomposition in Network Data Envelopment Analysis," International Series in Operations Research & Management Science, in: Wade D. Cook & Joe Zhu (ed.), Data Envelopment Analysis, edition 127, chapter 0, pages 55-77, Springer.
    31. Tone, Kaoru & Tsutsui, Miki, 2009. "Network DEA: A slacks-based measure approach," European Journal of Operational Research, Elsevier, vol. 197(1), pages 243-252, August.
    32. Kao, Chiang, 2015. "Efficiency measurement for hierarchical network systems," Omega, Elsevier, vol. 51(C), pages 121-127.
    33. Liang Liang & Feng Yang & Wade Cook & Joe Zhu, 2006. "DEA models for supply chain efficiency evaluation," Annals of Operations Research, Springer, vol. 145(1), pages 35-49, July.
    34. Cui, Qiang & Li, Ye, 2017. "Airline efficiency measures using a Dynamic Epsilon-Based Measure model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 100(C), pages 121-134.
    35. Liu, John S. & Lu, Louis Y.Y. & Lu, Wen-Min & Lin, Bruce J.Y., 2013. "A survey of DEA applications," Omega, Elsevier, vol. 41(5), pages 893-902.
    36. Liu, John S. & Lu, Louis Y.Y. & Lu, Wen-Min, 2016. "Research fronts in data envelopment analysis," Omega, Elsevier, vol. 58(C), pages 33-45.
    37. Wade Cook & Dan Chai & John Doyle & Rodney Green, 1998. "Hierarchies and Groups in DEA," Journal of Productivity Analysis, Springer, vol. 10(2), pages 177-198, October.
    38. Amado, Carla A.F. & São José, José M.S. & Santos, Sérgio P., 2016. "Measuring active ageing: A Data Envelopment Analysis approach," European Journal of Operational Research, Elsevier, vol. 255(1), pages 207-223.
    39. Cook, Wade D. & Zhu, Joe & Bi, Gongbing & Yang, Feng, 2010. "Network DEA: Additive efficiency decomposition," European Journal of Operational Research, Elsevier, vol. 207(2), pages 1122-1129, December.
    40. Chen, Chien-Ming, 2009. "A network-DEA model with new efficiency measures to incorporate the dynamic effect in production networks," European Journal of Operational Research, Elsevier, vol. 194(3), pages 687-699, May.
    41. Chen, Nengcheng & Xu, Lei & Chen, Zeqiang, 2017. "Environmental efficiency analysis of the Yangtze River Economic Zone using super efficiency data envelopment analysis (SEDEA) and tobit models," Energy, Elsevier, vol. 134(C), pages 659-671.
    42. Lim, Sungmook & Zhu, Joe, 2019. "Primal-dual correspondence and frontier projections in two-stage network DEA models," Omega, Elsevier, vol. 83(C), pages 236-248.
    43. Mahajan, Jayashree, 1991. "A data envelopment analytic model for assessing the relative efficiency of the selling function," European Journal of Operational Research, Elsevier, vol. 53(2), pages 189-205, July.
    44. Ma-Lin Song & Ron Fisher & Jian-Lin Wang & Lian-Biao Cui, 2018. "Environmental performance evaluation with big data: theories and methods," Annals of Operations Research, Springer, vol. 270(1), pages 459-472, November.
    45. Liang Liang & Wade D. Cook & Joe Zhu, 2008. "DEA models for two‐stage processes: Game approach and efficiency decomposition," Naval Research Logistics (NRL), John Wiley & Sons, vol. 55(7), pages 643-653, October.
    46. Cook, Wade D. & Ramón, Nuria & Ruiz, José L. & Sirvent, Inmaculada & Zhu, Joe, 2019. "DEA-based benchmarking for performance evaluation in pay-for-performance incentive plans," Omega, Elsevier, vol. 84(C), pages 45-54.
    47. Kao, Chiang & Hwang, Shiuh-Nan, 2008. "Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan," European Journal of Operational Research, Elsevier, vol. 185(1), pages 418-429, February.
    48. Cui, Qiang & Li, Ye & Wei, Yi-Ming, 2017. "Exploring the impacts of EU ETS on the pollution abatement costs of European airlines: An application of Network Environmental Production Function," Transport Policy, Elsevier, vol. 60(C), pages 131-142.
    49. Chang, Young-Tae & (Kevin) Park, Hyosoo & Zou, Bo & Kafle, Nabin, 2016. "Passenger facility charge vs. airport improvement program funds: A dynamic network DEA analysis for U.S. airport financing," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 88(C), pages 76-93.
    50. Rubén Elvira Herranz & Pablo García Estévez & María Auxiliadora de Vicente y Oliva & Rahul Dé, 2017. "Leveraging financial management performance of the Spanish aerospace manufacturing value chain," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 18(5), pages 1005-1022, September.
    51. Liu, Dan, 2017. "Evaluating the multi-period efficiency of East Asia airport companies," Journal of Air Transport Management, Elsevier, vol. 59(C), pages 71-82.
    52. Kaoru Tone & Miki Tsutsui, 2014. "Slacks-Based Network DEA," International Series in Operations Research & Management Science, in: Wade D. Cook & Joe Zhu (ed.), Data Envelopment Analysis, edition 127, chapter 0, pages 231-259, Springer.
    53. Jun-Fei Chu & Jie Wu & Ma-Lin Song, 2018. "An SBM-DEA model with parallel computing design for environmental efficiency evaluation in the big data context: a transportation system application," Annals of Operations Research, Springer, vol. 270(1), pages 105-124, November.
    54. Omrani, Hashem & Soltanzadeh, Elham, 2016. "Dynamic DEA models with network structure: An application for Iranian airlines," Journal of Air Transport Management, Elsevier, vol. 57(C), pages 52-61.
    55. Mallikarjun, Sreekanth, 2015. "Efficiency of US airlines: A strategic operating model," Journal of Air Transport Management, Elsevier, vol. 43(C), pages 46-56.
    56. Yao Chen & Wade D. Cook & Sungmook Lim, 2019. "Preface: DEA and its applications in operations and data analytics," Annals of Operations Research, Springer, vol. 278(1), pages 1-4, July.
    57. Yu, Ming-Miin & Chen, Li-Hsueh, 2016. "Centralized resource allocation with emission resistance in a two-stage production system: Evidence from a Taiwan’s container shipping company," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 650-671.
    58. Hashem Omrani & Mehdi Keshavarz, 2016. "A performance evaluation model for supply chain of shipping company in Iran: an application of the relational network DEA," Maritime Policy & Management, Taylor & Francis Journals, vol. 43(1), pages 121-135, January.
    59. Misiunas, Nicholas & Oztekin, Asil & Chen, Yao & Chandra, Kavitha, 2016. "DEANN: A healthcare analytic methodology of data envelopment analysis and artificial neural networks for the prediction of organ recipient functional status," Omega, Elsevier, vol. 58(C), pages 46-54.
    60. Kao, Chiang, 2018. "A classification of slacks-based efficiency measures in network data envelopment analysis with an analysis of the properties possessed," European Journal of Operational Research, Elsevier, vol. 270(3), pages 1109-1121.
    61. Yu, Ming-Miin & Chen, Li-Hsueh & Chiang, Hui, 2017. "The effects of alliances and size on airlines’ dynamic operational performance," Transportation Research Part A: Policy and Practice, Elsevier, vol. 106(C), pages 197-214.
    62. Cook, Wade D. & Liang, Liang & Zhu, Joe, 2010. "Measuring performance of two-stage network structures by DEA: A review and future perspective," Omega, Elsevier, vol. 38(6), pages 423-430, December.
    63. lo Storto, Corrado, 2018. "The analysis of the cost-revenue production cycle efficiency of the Italian airports: A NSBM DEA approach," Journal of Air Transport Management, Elsevier, vol. 72(C), pages 77-85.
    64. Sebastián Lozano & Ester Gutiérrez, 2014. "A slacks-based network DEA efficiency analysis of European airlines," Transportation Planning and Technology, Taylor & Francis Journals, vol. 37(7), pages 623-637, October.
    65. Shih-Liang Chao, 2017. "Integrating multi-stage data envelopment analysis and a fuzzy analytical hierarchical process to evaluate the efficiency of major global liner shipping companies," Maritime Policy & Management, Taylor & Francis Journals, vol. 44(4), pages 496-511, May.
    66. Cui, Qiang & Li, Ye & Lin, Jing-ling, 2018. "Pollution abatement costs change decomposition for airlines: An analysis from a dynamic perspective," Transportation Research Part A: Policy and Practice, Elsevier, vol. 111(C), pages 96-107.
    67. Joe Zhu, 2014. "Data Envelopment Analysis," International Series in Operations Research & Management Science, in: Quantitative Models for Performance Evaluation and Benchmarking, edition 3, chapter 1, pages 1-9, Springer.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gulati, Rachita & Charles, Vincent & Hassan, M. Kabir & Kumar, Sunil, 2023. "COVID-19 crisis and the efficiency of Indian banks: Have they weathered the storm?," Socio-Economic Planning Sciences, Elsevier, vol. 88(C).
    2. Huaqing Wu & Jingyu Yang & Wensheng Wu & Ya Chen, 2023. "Interest rate liberalization and bank efficiency: A DEA analysis of Chinese commercial banks," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 31(2), pages 467-498, June.
    3. Adjei Peter Darko & Decui Liang & Yinrunjie Zhang & Agbodah Kobina, 2023. "Service quality in football tourism: an evaluation model based on online reviews and data envelopment analysis with linguistic distribution assessments," Annals of Operations Research, Springer, vol. 325(1), pages 185-218, June.
    4. Suriyan Jomthanachai & Wai Peng Wong & Khai Wah Khaw, 2024. "An Application of Machine Learning to Logistics Performance Prediction: An Economics Attribute-Based of Collective Instance," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 741-792, February.
    5. Zhu, Qingyuan & Xu, Shuqi & Sun, Jiasen & Li, Xingchen & Zhou, Dequn, 2022. "Energy efficiency evaluation of power supply system: A data-driven approach based on shared resources," Applied Energy, Elsevier, vol. 312(C).
    6. Margherita Bernabei & Marco Eugeni & Paolo Gaudenzi & Francesco Costantino, 2023. "Assessment of Smart Transformation in the Manufacturing Process of Aerospace Components Through a Data-Driven Approach," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(1), pages 67-86, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Linyan & Chen, Kun, 2019. "Hierarchical network systems: An application to high-technology industry in China," Omega, Elsevier, vol. 82(C), pages 118-131.
    2. Tatiana Bencova & Andrea Bohacikova, 2022. "DEA in Performance Measurement of Two-Stage Processes: Comparative Overview of the Literature," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 5, pages 111-129.
    3. Kao, Chiang, 2014. "Network data envelopment analysis: A review," European Journal of Operational Research, Elsevier, vol. 239(1), pages 1-16.
    4. Liu, John S. & Lu, Louis Y.Y. & Lu, Wen-Min, 2016. "Research fronts in data envelopment analysis," Omega, Elsevier, vol. 58(C), pages 33-45.
    5. Lim, Dong-Joon & Kim, Moon-Su, 2022. "Measuring dynamic efficiency with variable time lag effects," Omega, Elsevier, vol. 108(C).
    6. Khezrimotlagh, Dariush & Kaffash, Sepideh & Zhu, Joe, 2022. "U.S. airline mergers’ performance and productivity change," Journal of Air Transport Management, Elsevier, vol. 102(C).
    7. Kaffash, Sepideh & Azizi, Roza & Huang, Ying & Zhu, Joe, 2020. "A survey of data envelopment analysis applications in the insurance industry 1993–2018," European Journal of Operational Research, Elsevier, vol. 284(3), pages 801-813.
    8. Ming-Fu Hsu & Ying-Shao Hsin & Fu-Jiing Shiue, 2022. "Business analytics for corporate risk management and performance improvement," Annals of Operations Research, Springer, vol. 315(2), pages 629-669, August.
    9. Nafiseh Javaherian & Ali Hamzehee & Hossein Sayyadi Tooranloo, 2021. "A compositional approach to two-stage Data Envelopment Analysis in intuitionistic fuzzy environment," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 31(1), pages 21-39.
    10. Nafiseh Javaherian & Ali Hamzehee & Hossein Sayyadi Tooranloo, 2021. "A compositional approach to two-stage Data Envelopment Analysis in intuitionistic fuzzy environment," Operations Research and Decisions, Wroclaw University of Science Technology, Faculty of Management, vol. 31, pages 21-39.
    11. Kao, Chiang, 2019. "Inefficiency identification for closed series production systems," European Journal of Operational Research, Elsevier, vol. 275(2), pages 599-607.
    12. Kao, Chiang, 2016. "Efficiency decomposition and aggregation in network data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 255(3), pages 778-786.
    13. Kottas, Angelos T. & Madas, Michael A., 2018. "Comparative efficiency analysis of major international airlines using Data Envelopment Analysis: Exploring effects of alliance membership and other operational efficiency determinants," Journal of Air Transport Management, Elsevier, vol. 70(C), pages 1-17.
    14. Chen, Ya & Li, Yongjun & Liang, Liang & Salo, Ahti & Wu, Huaqing, 2016. "Frontier projection and efficiency decomposition in two-stage processes with slacks-based measures," European Journal of Operational Research, Elsevier, vol. 250(2), pages 543-554.
    15. Yu, Ming-Miin & Rakshit, Ipsita, 2023. "Assessing the dynamic efficiency and technology gap of airports under different ownerships: A union dynamic NDEA approach," Omega, Elsevier, vol. 119(C).
    16. Kao, Chiang, 2020. "Decomposition of slacks-based efficiency measures in network data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 283(2), pages 588-600.
    17. Losa, Eduardo Tola & Arjomandi, Amir & Hervé Dakpo, K. & Bloomfield, Jason, 2020. "Efficiency comparison of airline groups in Annex 1 and non-Annex 1 countries: A dynamic network DEA approach," Transport Policy, Elsevier, vol. 99(C), pages 163-174.
    18. Qingxian An & Fanyong Meng & Beibei Xiong & Zongrun Wang & Xiaohong Chen, 2020. "Assessing the relative efficiency of Chinese high-tech industries: a dynamic network data envelopment analysis approach," Annals of Operations Research, Springer, vol. 290(1), pages 707-729, July.
    19. Chen, Kun & Zhu, Joe, 2020. "Additive slacks-based measure: Computational strategy and extension to network DEA," Omega, Elsevier, vol. 91(C).
    20. Chu, Junfei & Zhu, Joe, 2021. "Production scale-based two-stage network data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 294(1), pages 283-294.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:309:y:2022:i:2:d:10.1007_s10479-020-03668-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.