IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/96681.html
   My bibliography  Save this paper

Ranking of company performance indicators for managerial decision making purposes with application of the Delphi method

Author

Listed:
  • Gawlik, Remigiusz

Abstract

The objective of the paper is to analyse the usefulness of various qualitative and quantitative indicators of economic condition of companies for managerial decision-making purposes. The research target group were medium- and high level executives of small and medium enterprises with Polish and foreign capital, operating locally and internationally. The research methodology included: (i) Delphi questionnaire for quantitative data gathering; (ii) two-stage direct semi-structured interviews for initial reduction of number of indexes and to provide qualitative context for gathered data; (iii) ABC method (Pareto-Lorenz diagram) for presentation and interpretation of findings. In result a set of universal indicators has been determined, counting such indexes as: (i) flexibility, (ii) level of income; (iii) number of clients; (iv) survival ratio. Practical implication is a faster and more accurate choice of indexes enhancing the speed and efficiency of managerial decision-making. Further research should be directed towards the elaboration of a multicriteria decision-making model, which would allow to incorporate various types of indicators of company’s development, including the qualitative and quantitative ones. Research limitations come mainly from limited representativeness of chosen enterprises (they could be more specifically narrowed) and respondents. Presented research contributes to the increase of applicability of scientific tools for enhancement of managerial decision-making. Added value comes from addressing the need of managers for ease and rapidity of use of scientific decision-making methods.

Suggested Citation

  • Gawlik, Remigiusz, 2019. "Ranking of company performance indicators for managerial decision making purposes with application of the Delphi method," MPRA Paper 96681, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:96681
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/96681/1/MPRA_paper_96681.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    2. Marisa Ramírez Alesón & Manuel Espitia Escuer, 2002. "The impact of product diversification strategy on the corporate performance of large Spanish firms," Spanish Economic Review, Springer;Spanish Economic Association, vol. 4(2), pages 119-137.
    3. Jan Brzozowski & Marco Cucculelli, 2016. "Proactive and Reactive Attitude to Crisis: Evidence from European Firms," Entrepreneurial Business and Economics Review, Centre for Strategic and International Entrepreneurship at the Cracow University of Economics., vol. 4(1), pages 181-191.
    4. Łukasz Bryl & Szymon Truskolaski, 2017. "Human Capital Reporting and Its Determinants by Polish and German Publicly Listed Companies," Entrepreneurial Business and Economics Review, Centre for Strategic and International Entrepreneurship at the Cracow University of Economics., vol. 5(2), pages 195-210.
    Full references (including those not matched with items on IDEAS)

    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. Franz R. Hahn, 2007. "Determinants of Bank Efficiency in Europe. Assessing Bank Performance Across Markets," WIFO Studies, WIFO, number 31499.
    2. repec:lan:wpaper:1115 is not listed on IDEAS
    3. Azarnoosh Kafi & Behrouz Daneshian & Mohsen Rostamy-Malkhalifeh, 2021. "Forecasting the confidence interval of efficiency in fuzzy DEA," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 31(1), pages 41-59.
    4. Costa, Marcelo Azevedo & Lopes, Ana Lúcia Miranda & de Pinho Matos, Giordano Bruno Braz, 2015. "Statistical evaluation of Data Envelopment Analysis versus COLS Cobb–Douglas benchmarking models for the 2011 Brazilian tariff revision," Socio-Economic Planning Sciences, Elsevier, vol. 49(C), pages 47-60.
    5. Kristiaan Kerstens & Ignace Van de Woestyne, 2018. "Enumeration algorithms for FDH directional distance functions under different returns to scale assumptions," Annals of Operations Research, Springer, vol. 271(2), pages 1067-1078, December.
    6. Ahmad, Usman, 2011. "Financial Reforms and Banking Efficiency: Case of Pakistan," MPRA Paper 34220, University Library of Munich, Germany.
    7. Bowlin, W. F., 1995. "A characterization of the financial condition of the United States' aerospace-defense industrial base," Omega, Elsevier, vol. 23(5), pages 539-555, October.
    8. Büschken, Joachim, 2009. "When does data envelopment analysis outperform a naïve efficiency measurement model?," European Journal of Operational Research, Elsevier, vol. 192(2), pages 647-657, January.
    9. António Afonso & Ana Patricia Montes & José M. Domínguez, 2024. "Measuring Tax Burden Efficiency in OECD Countries: An International Comparison," CESifo Working Paper Series 11333, CESifo.
    10. Helmi Hammami & Thanh Ngo & David Tripe & Dinh-Tri Vo, 2022. "Ranking with a Euclidean common set of weights in data envelopment analysis: with application to the Eurozone banking sector," Annals of Operations Research, Springer, vol. 311(2), pages 675-694, April.
    11. Khanal, Aditya & Koirala, Krishna & Regmi, Madhav, 2016. "Do Financial Constraints Affect Production Efficiency in Drought Prone Areas? A Case from Indonesian Rice Growers," 2016 Annual Meeting, February 6-9, 2016, San Antonio, Texas 230087, Southern Agricultural Economics Association.
    12. Jahangoshai Rezaee, Mustafa & Jozmaleki, Mehrdad & Valipour, Mahsa, 2018. "Integrating dynamic fuzzy C-means, data envelopment analysis and artificial neural network to online prediction performance of companies in stock exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 489(C), pages 78-93.
    13. Vuciterna, Rina & Thomsen, Michael & Popp, Jennie & Musliu, Arben, 2017. "Efficiency and Competitiveness of Kosovo Raspberry Producers," 2017 Annual Meeting, February 4-7, 2017, Mobile, Alabama 252770, Southern Agricultural Economics Association.
    14. Bogetoft, Peter & Nielsen, Kurt, 2003. "Yardstick Based Procurement Design In Natural Resource Management," 2003 Annual Meeting, August 16-22, 2003, Durban, South Africa 25910, International Association of Agricultural Economists.
    15. Singer, Marcos & Donoso, Patricio & Poblete, Francisco, 2002. "Semi-autonomous planning using linear programming in the Chilean General Treasury," European Journal of Operational Research, Elsevier, vol. 140(2), pages 517-529, July.
    16. Chai, Naijie & Zhou, Wenliang & Hu, Xinlei, 2022. "Safety evaluation of urban rail transit operation considering uncertainty and risk preference: A case study in China," Transport Policy, Elsevier, vol. 125(C), pages 267-288.
    17. Fang, Lei, 2022. "Measuring and decomposing group performance under centralized management," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1006-1013.
    18. Chih-HAI YANG & Leah WU & Hui-Lin LIN, 2010. "Analysis of total-factor cultivated land efficiency in China's agriculture," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 56(5), pages 231-242.
    19. Jinyi Hu, 2023. "Linguistic Multiple-Attribute Decision Making Based on Regret Theory and Minimax-DEA," Mathematics, MDPI, vol. 11(20), pages 1-14, October.
    20. António Afonso & José Alves, 2023. "Are fiscal consolidation episodes helpful for public sector efficiency?," Applied Economics, Taylor & Francis Journals, vol. 55(31), pages 3547-3560, July.
    21. Chen, Yufeng & Ni, Liangfu & Liu, Kelong, 2021. "Does China's new energy vehicle industry innovate efficiently? A three-stage dynamic network slacks-based measure approach," Technological Forecasting and Social Change, Elsevier, vol. 173(C).

    More about this item

    Keywords

    managerial decision-making; enterprise development indicators; business management; small and medium enterprises;
    All these keywords.

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • M21 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics - - - Business Economics

    Statistics

    Access and download statistics

    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:pra:mprapa:96681. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    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.