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A hybrid fuzzy MCDM method for measuring the performance of publicly held pharmaceutical companies

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Listed:
  • Madjid Tavana
  • Kaveh Khalili-Damghani
  • Rahman Rahmatian

Abstract

Maximizing shareholders’ value has always been an indispensable goal for publicly traded companies. Shareholders value is highly dependent on the operating expenses, profit margin, return on investment and the overall performance in public companies. We propose a hybrid fuzzy multi-criteria decision making method for measuring the performance of publicly held companies in the Pharmaceutical industry. The proposed method helps investors choose a proper portfolio of stocks in the presence of environmental turbulence and uncertainties. The proposed method is composed of three distinct but inter-related phases. In the pre-screening phase, a set of financial and non-financial evaluation criteria are selected based on the balanced scorecard (BSC) approach. In the efficiency measurement phase, the DEMATEL method is used first to determine the inter-relationships among the BSC perspectives. A fuzzy ANP method is used next to determine the relative importance of the criteria based on the resulting DEMATEL interactive network. In the third step, two different fuzzy data envelopment analysis (DEA) methods are used to evaluate the relative efficiency of the decision making units (DMUs). The fuzzy DEA models are modified by using the relative importance of the criteria and the precedence relations among the input and output weights as additional constraints. Finally, the modified fuzzy DEA models are used to calculate the relative efficiency scores of the DMUs. In the ranking phase, an integration method grounded in the Shannon’s entropy concept is used to combine different efficiency scores and calculate the final ranking of the DMUs. The method proposed in this study is used to evaluate the performance of publicly held pharmaceutical companies actively trading on the Swiss Stock Exchange (SSE). Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Madjid Tavana & Kaveh Khalili-Damghani & Rahman Rahmatian, 2015. "A hybrid fuzzy MCDM method for measuring the performance of publicly held pharmaceutical companies," Annals of Operations Research, Springer, vol. 226(1), pages 589-621, March.
    • Handle: RePEc:spr:annopr:v:226:y:2015:i:1:p:589-621:10.1007/s10479-014-1738-8
    DOI: 10.1007/s10479-014-1738-8
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    Cited by:

    1. Shuli Liu & Xinwang Liu, 2016. "A Sample Survey Based Linguistic MADM Method with Prospect Theory for Online Shopping Problems," Group Decision and Negotiation, Springer, vol. 25(4), pages 749-774, July.
    2. Barak, Sasan & Dahooei, Jalil Heidary, 2018. "A novel hybrid fuzzy DEA-Fuzzy MADM method for airlines safety evaluation," Journal of Air Transport Management, Elsevier, vol. 73(C), pages 134-149.
    3. Ata Allah Taleizadeh & Elham Haji-Sami & Mahsa Noori-daryan, 2020. "A robust optimization model for coordinating pharmaceutical reverse supply chains under return strategies," Annals of Operations Research, Springer, vol. 291(1), pages 875-896, August.
    4. Deyun Zhou & Yongchuan Tang & Wen Jiang, 2017. "An Improved Belief Entropy and Its Application in Decision-Making," Complexity, Hindawi, vol. 2017, pages 1-15, March.
    5. Shiva Moslemi & Abolfazl Mirzazadeh & Gerhard-Wilhelm Weber & Mohammad Ali Sobhanallahi, 2022. "Integration of neural network and AP-NDEA model for performance evaluation of sustainable pharmaceutical supply chain," OPSEARCH, Springer;Operational Research Society of India, vol. 59(3), pages 1116-1157, September.
    6. Ahmet Kaya & Dragan Pamucar & Hasan Emin Gürler & Mehmet Ozcalici, 2024. "Determining the financial performance of the firms in the Borsa Istanbul sustainability index: integrating multi criteria decision making methods with simulation," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-44, December.
    7. Kheybari, Siamak & Rezaie, Fariba Mahdi & Farazmand, Hadis, 2020. "Analytic network process: An overview of applications," Applied Mathematics and Computation, Elsevier, vol. 367(C).
    8. Madjid Tavana & Kaveh Khalili-Damghani & Francisco J. Santos Arteaga & Arousha Hashemi, 2020. "A Malmquist productivity index for network production systems in the energy sector," Annals of Operations Research, Springer, vol. 284(1), pages 415-445, January.
    9. Afrasiabi, Ahmadreza & Chalmardi, Mazyar Kaboli & Balezentis, Tomas, 2022. "A novel hybrid evaluation framework for public organizations based on employees’ performance factors," Evaluation and Program Planning, Elsevier, vol. 91(C).

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