IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v250y2016i1p262-272.html
   My bibliography  Save this article

Performance measurement with multiple interrelated variables and threshold target levels: Evidence from retail firms in the US

Author

Listed:
  • Zervopoulos, Panagiotis D.
  • Brisimi, Theodora S.
  • Emrouznejad, Ali
  • Cheng, Gang

Abstract

In this study, we developed a DEA–based performance measurement methodology that is consistent with performance assessment frameworks such as the Balanced Scorecard. The methodology developed in this paper takes into account the direct or inverse relationships that may exist among the dimensions of performance to construct appropriate production frontiers. The production frontiers we obtained are deemed appropriate as they consist solely of firms with desirable levels for all dimensions of performance. These levels should be at least equal to the critical values set by decision makers. The properties and advantages of our methodology against competing methodologies are presented through an application to a real-world case study from retail firms operating in the US. A comparative analysis between the new methodology and existing methodologies explains the failure of the existing approaches to define appropriate production frontiers when directly or inversely related dimensions of performance are present and to express the interrelationships between the dimensions of performance.

Suggested Citation

  • Zervopoulos, Panagiotis D. & Brisimi, Theodora S. & Emrouznejad, Ali & Cheng, Gang, 2016. "Performance measurement with multiple interrelated variables and threshold target levels: Evidence from retail firms in the US," European Journal of Operational Research, Elsevier, vol. 250(1), pages 262-272.
    • Handle: RePEc:eee:ejores:v:250:y:2016:i:1:p:262-272
    DOI: 10.1016/j.ejor.2015.08.055
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221715008115
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2015.08.055?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. Cheng, Gang & Zervopoulos, Panagiotis D., 2014. "Estimating the technical efficiency of health care systems: A cross-country comparison using the directional distance function," European Journal of Operational Research, Elsevier, vol. 238(3), pages 899-910.
    2. Amado, Carla A.F. & Santos, Sérgio P. & Marques, Pedro M., 2012. "Integrating the Data Envelopment Analysis and the Balanced Scorecard approaches for enhanced performance assessment," Omega, Elsevier, vol. 40(3), pages 390-403.
    3. Leopold Simar & Paul Wilson, 2000. "A general methodology for bootstrapping in non-parametric frontier models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(6), pages 779-802.
    4. Färe, Rolf & Grosskopf, Shawna, 2010. "Directional distance functions and slacks-based measures of efficiency: Some clarifications," European Journal of Operational Research, Elsevier, vol. 206(3), pages 702-702, November.
    5. Alois Kneip & Léopold Simar & Paul Wilson, 2011. "A Computationally Efficient, Consistent Bootstrap for Inference with Non-parametric DEA Estimators," Computational Economics, Springer;Society for Computational Economics, vol. 38(4), pages 483-515, November.
    6. Kneip, Alois & Simar, Léopold & Wilson, Paul W., 2008. "Asymptotics And Consistent Bootstraps For Dea Estimators In Nonparametric Frontier Models," Econometric Theory, Cambridge University Press, vol. 24(6), pages 1663-1697, December.
    7. A Mukherjee & P Nath & M Pal, 2003. "Resource, service quality and performance triad: a framework for measuring efficiency of banking services," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(7), pages 723-735, July.
    8. 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.
    9. Eugene W. Anderson & Claes Fornell & Roland T. Rust, 1997. "Customer Satisfaction, Productivity, and Profitability: Differences Between Goods and Services," Marketing Science, INFORMS, vol. 16(2), pages 129-145.
    10. Andreas Soteriou & Stavros A. Zenios, 1999. "Operations, Quality, and Profitability in the Provision of Banking Services," Management Science, INFORMS, vol. 45(9), pages 1221-1238, September.
    11. Banker, Rajiv D. & Chang, Hsihui & Janakiraman, Surya N. & Konstans, Constantine, 2004. "A balanced scorecard analysis of performance metrics," European Journal of Operational Research, Elsevier, vol. 154(2), pages 423-436, April.
    12. R G Dyson & E A Shale, 2010. "Data envelopment analysis, operational research and uncertainty," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(1), pages 25-34, January.
    13. Léopold Simar & Paul W. Wilson, 1998. "Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models," Management Science, INFORMS, vol. 44(1), pages 49-61, January.
    14. Eilat, Harel & Golany, Boaz & Shtub, Avraham, 2008. "R&D project evaluation: An integrated DEA and balanced scorecard approach," Omega, Elsevier, vol. 36(5), pages 895-912, October.
    15. O. B. Olesen & N. C. Petersen, 1995. "Chance Constrained Efficiency Evaluation," Management Science, INFORMS, vol. 41(3), pages 442-457, March.
    16. Paradi, Joseph C. & Rouatt, Stephen & Zhu, Haiyan, 2011. "Two-stage evaluation of bank branch efficiency using data envelopment analysis," Omega, Elsevier, vol. 39(1), pages 99-109, January.
    17. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    18. Lim, Sungmook & Zhu, Joe, 2013. "Incorporating performance measures with target levels in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 230(3), pages 634-642.
    19. Färe, Rolf & Grosskopf, Shawna, 2010. "Directional distance functions and slacks-based measures of efficiency," European Journal of Operational Research, Elsevier, vol. 200(1), pages 320-322, January.
    20. Eilat, Harel & Golany, Boaz & Shtub, Avraham, 2006. "Constructing and evaluating balanced portfolios of R&D projects with interactions: A DEA based methodology," European Journal of Operational Research, Elsevier, vol. 172(3), pages 1018-1039, August.
    21. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    22. García-Valderrama, Teresa & Mulero-Mendigorri, Eva & Revuelta-Bordoy, Daniel, 2009. "Relating the perspectives of the balanced scorecard for R&D by means of DEA," European Journal of Operational Research, Elsevier, vol. 196(3), pages 1177-1189, August.
    23. Emrouznejad, Ali & Parker, Barnett R. & Tavares, Gabriel, 2008. "Evaluation of research in efficiency and productivity: A survey and analysis of the first 30 years of scholarly literature in DEA," Socio-Economic Planning Sciences, Elsevier, vol. 42(3), pages 151-157, September.
    24. H. Sherman & Joe Zhu, 2006. "Benchmarking with quality-adjusted DEA (Q-DEA) to seek lower-cost high-quality service: Evidence from a U.S.bank application," Annals of Operations Research, Springer, vol. 145(1), pages 301-319, July.
    25. Rajiv D. Banker, 1993. "Maximum Likelihood, Consistency and Data Envelopment Analysis: A Statistical Foundation," Management Science, INFORMS, vol. 39(10), pages 1265-1273, October.
    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. Costa Melo, Isotilia & Alves Junior, Paulo Nocera & Callefi, Jéssica Syrio & da Silva, Karoline Arguelho & Nagano, Marcelo Seido & Rebelatto, Daisy Aparecida do Nascimento & Rentizelas, Athanasios, 2023. "Measuring the performance of retailers during the COVID-19 pandemic: Embedding optimal control theory principles in a dynamic data envelopment analysis approach," Operations Research Perspectives, Elsevier, vol. 10(C).
    2. Tao Du, 2017. "Performance Measurement of Healthcare Service and Association Discussion between Quality and Efficiency: Evidence from 31 Provinces of Mainland China," Sustainability, MDPI, vol. 10(1), pages 1-19, December.
    3. Panagiotis Mitropoulos & Panagiotis D. Zervopoulos & Ioannis Mitropoulos, 2020. "Measuring performance in the presence of noisy data with targeted desirable levels: evidence from healthcare units," Annals of Operations Research, Springer, vol. 294(1), pages 537-566, November.
    4. Youchao Tan & Yang Zhang & Roohollah Khodaverdi, 2017. "Service performance evaluation using data envelopment analysis and balance scorecard approach: an application to automotive industry," Annals of Operations Research, Springer, vol. 248(1), pages 449-470, January.

    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. 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.
    2. Valentin Zelenyuk, 2019. "Data Envelopment Analysis and Business Analytics: The Big Data Challenges and Some Solutions," CEPA Working Papers Series WP072019, School of Economics, University of Queensland, Australia.
    3. Amado, Carla A.F. & Santos, Sérgio P. & Marques, Pedro M., 2012. "Integrating the Data Envelopment Analysis and the Balanced Scorecard approaches for enhanced performance assessment," Omega, Elsevier, vol. 40(3), pages 390-403.
    4. Davtalab-Olyaie, Mostafa & Asgharian, Masoud & Nia, Vahid Partovi, 2019. "Stochastic ranking and dominance in DEA," International Journal of Production Economics, Elsevier, vol. 214(C), pages 125-138.
    5. Zervopoulos, Panagiotis & Emrouznejad, Ali & Sklavos, Sokratis, 2019. "A Bayesian approach for correcting bias of data envelopment analysis estimators," MPRA Paper 91886, University Library of Munich, Germany.
    6. Arabmaldar, Aliasghar & Sahoo, Biresh K. & Ghiyasi, Mojtaba, 2023. "A generalized robust data envelopment analysis model based on directional distance function," European Journal of Operational Research, Elsevier, vol. 311(2), pages 617-632.
    7. Michali, Maria & Emrouznejad, Ali & Dehnokhalaji, Akram & Clegg, Ben, 2023. "Subsampling bootstrap in network DEA," European Journal of Operational Research, Elsevier, vol. 305(2), pages 766-780.
    8. George Halkos & Roman Matousek & Nickolaos Tzeremes, 2016. "Pre-evaluating technical efficiency gains from possible mergers and acquisitions: evidence from Japanese regional banks," Review of Quantitative Finance and Accounting, Springer, vol. 46(1), pages 47-77, January.
    9. Behrouz Arabi & Susila Munisamy Doraisamy & Ali Emrouznejad & Alireza Khoshroo, 2017. "Eco-efficiency measurement and material balance principle: an application in power plants Malmquist Luenberger Index," Annals of Operations Research, Springer, vol. 255(1), pages 221-239, August.
    10. Léopold Simar & Paul W. Wilson, 2015. "Statistical Approaches for Non-parametric Frontier Models: A Guided Tour," International Statistical Review, International Statistical Institute, vol. 83(1), pages 77-110, April.
    11. Youchao Tan & Yang Zhang & Roohollah Khodaverdi, 2017. "Service performance evaluation using data envelopment analysis and balance scorecard approach: an application to automotive industry," Annals of Operations Research, Springer, vol. 248(1), pages 449-470, January.
    12. Chiang Kao & Shiang-Tai Liu, 2022. "Stochastic efficiencies of network production systems with correlated stochastic data: the case of Taiwanese commercial banks," Annals of Operations Research, Springer, vol. 315(2), pages 1151-1174, August.
    13. Kao, Chiang & Liu, Shiang-Tai, 2009. "Stochastic data envelopment analysis in measuring the efficiency of Taiwan commercial banks," European Journal of Operational Research, Elsevier, vol. 196(1), pages 312-322, July.
    14. José Solana‐Ibáñez & Manuel Caravaca‐Garratón & Ricardo Teruel‐Sánchez, 2020. "Stakeholder perception on corporate reputation and management efficiency: Evidence from the Spanish Defence sector," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 27(5), pages 2381-2399, September.
    15. George E. Halkos & Roman Matousek & Nickolaos G. Tzeremes, 2016. "Pre-evaluating technical efficiency gains from possible mergers and acquisitions: evidence from Japanese regional banks," Review of Quantitative Finance and Accounting, Springer, vol. 46(1), pages 47-77, January.
    16. Aggelopoulos, Eleftherios & Georgopoulos, Antonios, 2017. "Bank branch efficiency under environmental change: A bootstrap DEA on monthly profit and loss accounting statements of Greek retail branches," European Journal of Operational Research, Elsevier, vol. 261(3), pages 1170-1188.
    17. Chien-Ming Chen & Magali A. Delmas, 2012. "Measuring Eco-Inefficiency: A New Frontier Approach," Operations Research, INFORMS, vol. 60(5), pages 1064-1079, October.
    18. Luis R. Murillo‐Zamorano, 2004. "Economic Efficiency and Frontier Techniques," Journal of Economic Surveys, Wiley Blackwell, vol. 18(1), pages 33-77, February.
    19. Simar, Léopold & Vanhems, Anne & Wilson, Paul W., 2012. "Statistical inference for DEA estimators of directional distances," European Journal of Operational Research, Elsevier, vol. 220(3), pages 853-864.
    20. Halkos, George & Petrou, Kleoniki Natalia, 2019. "Treating undesirable outputs in DEA: A critical review," Economic Analysis and Policy, Elsevier, vol. 62(C), pages 97-104.

    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:eee:ejores:v:250:y:2016:i:1:p:262-272. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    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.