Open Access
Issue
RAIRO-Oper. Res.
Volume 58, Number 4, July-August 2024
Page(s) 3107 - 3117
DOI https://doi.org/10.1051/ro/2024091
Published online 01 August 2024
  • G.R. Amin and M. Ibn Boamah, A new inverse DEA cost efficiency model for estimating potential merger gains: a case of Canadian banks. Ann. Oper. Res. 295 (2020) 21–36. [CrossRef] [MathSciNet] [Google Scholar]
  • G.R. Amin and M. Ibn Boamah, A two-stage inverse data envelopment analysis approach for estimating potential merger gains in the US banking sector. Manage. Decis. Econ. 42 (2021) 1454–1465. [CrossRef] [Google Scholar]
  • G.R. Amin and M. Ibn Boamah, Modeling business partnerships: a data envelopment analysis approach. Eur. J. Oper. Res. 305 (2023) 329–337. [CrossRef] [Google Scholar]
  • G.R. Amin, A. Emrouznejad and S. Gattoufi, Modelling generalized firms’ restructuring using inverse DEA. J. Prod. Anal. 48 (2017) 51–61. [Google Scholar]
  • G.R. Amin, S. Al-Muharrami and M. Toloo, A combined goal programming and inverse DEA method for target setting in mergers. Expert Syst. App. 115 (2019) 412–417. [CrossRef] [Google Scholar]
  • G. Buchak, G. Matvos, T. Piskorski and A. Seru, Fintech, regulatory arbitrage, and the rise of shadow banks. J. Finan. Econ. 130 (2018) 453–483. [CrossRef] [Google Scholar]
  • Y. Cheng, Y. Li and Y. Li, An optimization model of partner selection of joint distribution alliance considering the supply and demand matching degree. IEEE Access 7 (2019) 162454–162464. [CrossRef] [Google Scholar]
  • S. Gattoufi, G.R. Amin and A. Emrouznejad, A new inverse DEA method for merging banks. IMA J. Manage. Math. 25 (2014) 73–87. [Google Scholar]
  • R. Hlioui, A. Gharbi and A. Hajji, Joint supplier selection, production and replenishment of an unreliable manufacturing-oriented supply chain. Int. J. Prod. Econ. 187 (2017) 53–67. [Google Scholar]
  • S. Lozano, Using DEA to find the best partner for a horizontal cooperation. Comput. Ind. Eng. 66 (2013) 286–292. [CrossRef] [Google Scholar]
  • F. Niepmann and T. Schmidt-Eisenlohr, International trade, risk and the role of banks. J. Int. Econ. 107 (2017) 111–126. [CrossRef] [Google Scholar]
  • D.T. Robinson, Strategic alliances and the boundaries of the firm. Rev. Finan. Stud. 21 (2008) 649–681. [CrossRef] [Google Scholar]
  • M. Tavana, A.K. Nasr, H. Mina and J. Michnik, A private sustainable partner selection model for green public-private partnerships and regional economic development. Soc.-Econ. Planning Sci. 83 (2022) 101189. [CrossRef] [Google Scholar]
  • K. Wang, W. Huang, J. Wu and Y. Liu, Efficiency measures of the Chinese commercial banking system using an additive two-stage DEA. Omega 44 (2014) 5–20. [CrossRef] [Google Scholar]
  • C. Wang, R. Wu and L. Deng, An incentive mechanism for partner selection from a collaborative network with private information. Comput. Ind. Eng. 154 (2021) 107053. [CrossRef] [Google Scholar]
  • J. Wu, Q. Zhu, W. Cook and J. Zhu, Best cooperative partner selection and input resource reallocation using DEA. J. Oper. Res. Soc. 67 (2016) 1221–1237. [CrossRef] [Google Scholar]
  • C. Wu, Y. Zhang, H. Pun and C. Lin, Construction of partner selection criteria in sustainable supply chains: a systematic optimization model. Expert Syst. App. 158 (2020) 113643. [CrossRef] [Google Scholar]
  • J. Zhao, X. Deng, Y. Lin and J. Lu, Multi-objective enterprise partner selection model with different relative superiority parameters based on particle swarm optimization, in Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2019). Advances in Intelligent Systems and Computing, edited by C. Huang, Y.W. Chan and N. Yen. Springer, Singapore (2020) 1088. [Google Scholar]
  • Q. Zhu, J. Wu, J. Chu, A. Amirteimoori and J. Sun, DEA-based models for best partner selection for merger. RAIRO Oper. Res. 51 (2017) 1345–1357. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
  • Q. Zhu, X. Li, F. Li and A. Amirteimoori, Data-driven approach to find the best partner for merger and acquisitions in banking industry. Ind. Manage. Data Syst. 121 (2021) 879–893. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.