IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i20p5667-d276337.html
   My bibliography  Save this article

Eliciting Weights of Significance of Criteria for a Monitoring Model of Performance of SMEs for Successful Insolvency Administrator’s Intervention

Author

Listed:
  • Askoldas Podviezko

    (Agricultural Policy and Foreign Trade Division, Lithuanian Institute of Agrarian Economics, LT-03105 Vilnius, Lithuania
    These authors contributed equally to this work.)

  • Ralph Kurschus

    (Rechtsanwalte—Insolvenzverwalter, Schwedenstraße 11, DE-17033 Neubrandenburg, Germany
    These authors contributed equally to this work.)

  • Giedre Lapinskiene

    (Department of Business Technologies and Entrepreneurship, Vilnius Gediminas Technical University, Sauletekio ave, LT-10223 Vilnius, Lithuania
    These authors contributed equally to this work.)

Abstract

Small and medium-sized enterprises (SMEs) are accounted for as a major part of the economy of the EU in terms of part of the population employed, turnover, value-added, etc. Causes of insolvency of SMEs can be different; they are categorized in the paper. A considerable shift from resolving cases of bankruptcy with the sole aim to satisfy creditors’ rights to augmenting and enhancing liquidation and reorganization procedures evolved interest of the authors in creating efficient bankruptcy prediction models and, in particular, methodologies for evaluation and monitoring of the performance of SMEs. In the paper, we reviewed several initiatives and instruments created by the EU for supporting SMEs. The paper laid a foundation for creating a more comprehensive methodology for evaluation of the state of a firm undergoing the process of reorganization. A hierarchy structure of criteria for the evaluation of SMEs was used in the paper; methodologies for eliciting weights of importance of criteria from experts and gauging the level of concordance of opinions of experts were applied. Resulting weights of criteria of performance of an insolvent SME were obtained; the importance of the managerial category of criteria was revealed. Prominent features of hierarchy structures and methodology of using the structure for calculating ultimate weights were described and demonstrated. Gauging concordance of opinions of experts revealed a satisfactory level of concordance of opinions of experts; this allowed to prepare the ultimate weights of criteria for multiple criteria evaluation of SMEs for further research.

Suggested Citation

  • Askoldas Podviezko & Ralph Kurschus & Giedre Lapinskiene, 2019. "Eliciting Weights of Significance of Criteria for a Monitoring Model of Performance of SMEs for Successful Insolvency Administrator’s Intervention," Sustainability, MDPI, vol. 11(20), pages 1-16, October.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:20:p:5667-:d:276337
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/20/5667/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/11/20/5667/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. World Bank, 2018. "Doing Business 2018," World Bank Publications - Books, The World Bank Group, number 28608, December.
    2. Marija Burinskienė & Vytautas Bielinskas & Askoldas Podviezko & Virginija Gurskienė & Vida Maliene, 2017. "Evaluating the Significance of Criteria Contributing to Decision-Making on Brownfield Land Redevelopment Strategies in Urban Areas," Sustainability, MDPI, vol. 9(5), pages 1-17, May.
    3. Edmundas Kazimieras Zavadskas & Valentinas Podvezko, 2016. "Integrated Determination of Objective Criteria Weights in MCDM," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(02), pages 267-283, March.
    4. Gupta, Jairaj & Gregoriou, Andros, 2018. "Impact of market-based finance on SMEs failure," Economic Modelling, Elsevier, vol. 69(C), pages 13-25.
    5. Tian, Shaonan & Yu, Yan, 2017. "Financial ratios and bankruptcy predictions: An international evidence," International Review of Economics & Finance, Elsevier, vol. 51(C), pages 510-526.
    6. Ausloos, Marcel & Cerqueti, Roy & Bartolacci, Francesca & Castellano, Nicola G., 2018. "SME investment best strategies. Outliers for assessing how to optimize performance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 754-765.
    7. Askoldas Podviezko & Lyudmila Parfenova & Andrey Pugachev, 2019. "Tax Competitiveness of the New EU Member States," JRFM, MDPI, vol. 12(1), pages 1-19, February.
    8. Meghana Ayyagari & Thorsten Beck & Asli Demirguc-Kunt, 2007. "Small and Medium Enterprises Across the Globe," Small Business Economics, Springer, vol. 29(4), pages 415-434, December.
    9. Jean-Marie Courrent & Sonia Chassé & Waleed Omri, 2018. "Do Entrepreneurial SMEs Perform Better Because They are More Responsible?," Journal of Business Ethics, Springer, vol. 153(2), pages 317-336, December.
    10. Sabol, Andrija & Sverer, Filip, 2017. "A Review Of The Economic Value Added Literature And Application," UTMS Journal of Economics, University of Tourism and Management, Skopje, Macedonia, vol. 8(1), pages 19-27.
    11. Sahana Roy Chowdhury, 2011. "Impact of Global Crisis on Small and Medium Enterprises," Global Business Review, International Management Institute, vol. 12(3), pages 377-399, October.
    12. Carmine Bianchi & Federico Cosenz & Milica Marinković, 2015. "Designing dynamic performance management systems to foster SME competitiveness according to a sustainable development perspective: empirical evidences from a case-study," International Journal of Business Performance Management, Inderscience Enterprises Ltd, vol. 16(1), pages 84-108.
    13. Vytautas Palevičius & Askoldas Podviezko & Henrikas Sivilevičius & Olegas Prentkovskis, 2018. "Decision-Aiding Evaluation of Public Infrastructure for Electric Vehicles in Cities and Resorts of Lithuania," Sustainability, MDPI, vol. 10(4), pages 1-17, March.
    14. Edward I. Altman & Gabriele Sabato, 2013. "MODELING CREDIT RISK FOR SMEs: EVIDENCE FROM THE US MARKET," World Scientific Book Chapters, in: Oliviero Roggi & Edward I Altman (ed.), Managing and Measuring Risk Emerging Global Standards and Regulations After the Financial Crisis, chapter 9, pages 251-279, World Scientific Publishing Co. Pte. Ltd..
    15. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    16. Askoldas Podviezko, 2015. "Use of multiple criteria decision aid methods in case of large amounts of data," International Journal of Business and Emerging Markets, Inderscience Enterprises Ltd, vol. 7(2), pages 155-169.
    17. John Stookey & Michael Baer, 1976. "A critique of Guttman scaling: With special attention to its application to the study of collegial bodies," Quality & Quantity: International Journal of Methodology, Springer, vol. 10(3), pages 251-260, September.
    18. D. Marc Kilgour & Ye Chen & Keith W. Hipel, 2010. "Multiple Criteria Approaches to Group Decision and Negotiation," International Series in Operations Research & Management Science, in: Matthias Ehrgott & José Rui Figueira & Salvatore Greco (ed.), Trends in Multiple Criteria Decision Analysis, chapter 0, pages 317-338, Springer.
    19. Grice, John Stephen & Ingram, Robert W., 2001. "Tests of the generalizability of Altman's bankruptcy prediction model," Journal of Business Research, Elsevier, vol. 54(1), pages 53-61, October.
    20. Moujib Bahri & Josée St‐Pierre & Ouafa Sakka, 2011. "Economic value added: a useful tool for SME performance management," International Journal of Productivity and Performance Management, Emerald Group Publishing Limited, vol. 60(6), pages 603-621, July.
    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. Sanghoon Lee & Keunho Choi & Donghee Yoo, 2020. "Predicting the Insolvency of SMEs Using Technological Feasibility Assessment Information and Data Mining Techniques," Sustainability, MDPI, vol. 12(23), pages 1-17, November.

    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. Kaya, Orcun, 2022. "Determinants and consequences of SME insolvency risk during the pandemic," Economic Modelling, Elsevier, vol. 115(C).
    2. Lin, Hsiou-Wei William & Lo, Huai-Chun & Wu, Ruei-Shian, 2016. "Modeling default prediction with earnings management," Pacific-Basin Finance Journal, Elsevier, vol. 40(PB), pages 306-322.
    3. Askoldas Podviezko & Lyudmila Parfenova & Andrey Pugachev, 2019. "Tax Competitiveness of the New EU Member States," JRFM, MDPI, vol. 12(1), pages 1-19, February.
    4. Mohammad Mahdi Mousavi & Jamal Ouenniche & Kaoru Tone, 2023. "A dynamic performance evaluation of distress prediction models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 756-784, July.
    5. Jairaj Gupta & Andros Gregoriou & Jerome Healy, 2015. "Forecasting bankruptcy for SMEs using hazard function: To what extent does size matter?," Review of Quantitative Finance and Accounting, Springer, vol. 45(4), pages 845-869, November.
    6. Paulo V. Carvalho & José D. Curto & Rodrigo Primor, 2022. "Macroeconomic determinants of credit risk: Evidence from the Eurozone," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 2054-2072, April.
    7. fernández, María t. Tascón & gutiérrez, Francisco J. Castaño, 2012. "Variables y Modelos Para La Identificación y Predicción Del Fracaso Empresarial: Revisión de La Investigación Empírica Reciente," Revista de Contabilidad - Spanish Accounting Review, Elsevier, vol. 15(1), pages 7-58.
    8. Zhichao Luo & Pingyu Hsu & Ni Xu, 2020. "SME Default Prediction Framework with the Effective Use of External Public Credit Data," Sustainability, MDPI, vol. 12(18), pages 1-18, September.
    9. Ciampi, Francesco, 2015. "Corporate governance characteristics and default prediction modeling for small enterprises. An empirical analysis of Italian firms," Journal of Business Research, Elsevier, vol. 68(5), pages 1012-1025.
    10. Alessandro Bitetto & Stefano Filomeni & Michele Modina, 2021. "Understanding corporate default using Random Forest: The role of accounting and market information," DEM Working Papers Series 205, University of Pavia, Department of Economics and Management.
    11. Ashraf, Sumaira & Félix, Elisabete G.S. & Serrasqueiro, Zélia, 2020. "Development and testing of an augmented distress prediction model: A comparative study on a developed and an emerging market," Journal of Multinational Financial Management, Elsevier, vol. 57.
    12. Katarina Valaskova & Dominika Gajdosikova & Jaroslav Belas, 2023. "Bankruptcy prediction in the post-pandemic period: A case study of Visegrad Group countries," Oeconomia Copernicana, Institute of Economic Research, vol. 14(1), pages 253-293, March.
    13. Mauro Paoloni & Massimiliano Celli, 2018. "Crisi delle PMI e strumenti di warning. Un test di verifica nel settore manifatturiero," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2018(2), pages 85-106.
    14. Francesco Ciampi & Valentina Cillo & Fabio Fiano, 2020. "Combining Kohonen maps and prior payment behavior for small enterprise default prediction," Small Business Economics, Springer, vol. 54(4), pages 1007-1039, April.
    15. Li, Chunyu & Lou, Chenxin & Luo, Dan & Xing, Kai, 2021. "Chinese corporate distress prediction using LASSO: The role of earnings management," International Review of Financial Analysis, Elsevier, vol. 76(C).
    16. Maurice Peat, 2007. "Factors Affecting the Probability of Bankruptcy: A Managerial Decision Based Approach," Abacus, Accounting Foundation, University of Sydney, vol. 43(3), pages 303-324, September.
    17. Francesco Ciampi & Alessandro Giannozzi & Giacomo Marzi & Edward I. Altman, 2021. "Rethinking SME default prediction: a systematic literature review and future perspectives," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(3), pages 2141-2188, March.
    18. Chiara Pederzoli & Grid Thoma & Costanza Torricelli, 2013. "Modelling Credit Risk for Innovative SMEs: the Role of Innovation Measures," Journal of Financial Services Research, Springer;Western Finance Association, vol. 44(1), pages 111-129, August.
    19. Chen, An-Sing & Chu, Hsiang-Hui & Hung, Pi-Hsia & Cheng, Miao-Sih, 2020. "Financial risk and acquirers' stockholder wealth in mergers and acquisitions," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    20. Serrano-Cinca, Carlos & Gutiérrez-Nieto, Begoña & Bernate-Valbuena, Martha, 2019. "The use of accounting anomalies indicators to predict business failure," European Management Journal, Elsevier, vol. 37(3), pages 353-375.

    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:gam:jsusta:v:11:y:2019:i:20:p:5667-:d:276337. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.