IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i21p11934-d667009.html
   My bibliography  Save this article

Efficiency Assessment of Operations Strategy Matrix in Healthcare Systems of US States Amid COVID-19: Implications for Sustainable Development Goals

Author

Listed:
  • Aydın Özdemir

    (Department of Management, Faculty of Business Administration, Gebze Technical University, Kocaeli 41400, Turkey
    Besni Ali Erdemoğlu Vocational High School, Adiyaman University, Adiyaman 02300, Turkey)

  • Hakan Kitapçı

    (Department of Management, Faculty of Business Administration, Gebze Technical University, Kocaeli 41400, Turkey)

  • Mehmet Şahin Gök

    (Department of Management, Faculty of Business Administration, Gebze Technical University, Kocaeli 41400, Turkey)

  • Erşan Ciğerim

    (Department of Management, Faculty of Business Administration, Gebze Technical University, Kocaeli 41400, Turkey)

Abstract

The objective of this study is to assess the efficiency of the operations strategy matrix in the healthcare system of U.S. states amid COVID-19. Output-Oriented Data Envelopment Analysis was used to assess the efficiency of the operations strategy matrix. Strategic Decision Areas (Capacity, Supply Network, Process Technology, and Development and Organization) were considered inputs while competitive priorities (Quality, Cost, Delivery, and Flexibility) were considered outputs. According to results; Alaska, Alabama, Arkansans, Florida, Hawaii, Iowa, Idaho, Louisiana, Minnesota, Missouri, Mississippi, Montana, North Carolina, New Jersey, New York, Oklahoma, South Carolina, South Dakota, Texas, Vermont, Wisconsin, and Wyoming are relatively efficient. Additionally, Connecticut, Louisiana, Minnesota, New Jersey, Rhode Island, Tennessee, Utah, Vermont, Washington, and Wyoming are fully efficient while South Dakota is the state that needs the most improvement in terms of strategic decision areas and competing priorities. On the other hand, inefficient states have larger population and GDP than efficient states. Based on these results, implications for sustainable development goals (SDGs) are drawn.

Suggested Citation

  • Aydın Özdemir & Hakan Kitapçı & Mehmet Şahin Gök & Erşan Ciğerim, 2021. "Efficiency Assessment of Operations Strategy Matrix in Healthcare Systems of US States Amid COVID-19: Implications for Sustainable Development Goals," Sustainability, MDPI, vol. 13(21), pages 1-17, October.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:21:p:11934-:d:667009
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/21/11934/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/21/11934/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yasar A. Ozcan, 2014. "Evaluation of Performance in Health Care," International Series in Operations Research & Management Science, in: Health Care Benchmarking and Performance Evaluation, edition 2, chapter 0, pages 3-14, Springer.
    2. Golany, B & Roll, Y, 1989. "An application procedure for DEA," Omega, Elsevier, vol. 17(3), pages 237-250.
    3. Beata Gavurova & Kristina Kocisova & Jakub Sopko, 2021. "Health system efficiency in OECD countries: dynamic network DEA approach," Health Economics Review, Springer, vol. 11(1), pages 1-25, December.
    4. Alam, Shahriar Tanvir & Ahmed, Sayem & Ali, Syed Mithun & Sarker, Sudipa & Kabir, Golam & ul-Islam, Asif, 2021. "Challenges to COVID-19 vaccine supply chain: Implications for sustainable development goals," International Journal of Production Economics, Elsevier, vol. 239(C).
    5. Tomohiro Tasaki & Ryo Tajima & Yasuko Kameyama, 2021. "Measurement of the Importance of 11 Sustainable Development Criteria: How Do the Important Criteria Differ among Four Asian Countries and Shift as the Economy Develops?," Sustainability, MDPI, vol. 13(17), pages 1-17, August.
    6. Ray,Subhash C., 2012. "Data Envelopment Analysis," Cambridge Books, Cambridge University Press, number 9781107405264, January.
    7. Ali Emrouznejad & Emilyn Cabanda, 2014. "Managing Service Productivity Using Data Envelopment Analysis," International Series in Operations Research & Management Science, in: Ali Emrouznejad & Emilyn Cabanda (ed.), Managing Service Productivity, edition 127, pages 1-17, Springer.
    8. 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.
    9. Sakib, Nazmus & Ibne Hossain, Niamat Ullah & Nur, Farjana & Talluri, Srinivas & Jaradat, Raed & Lawrence, Jeanne Marie, 2021. "An assessment of probabilistic disaster in the oil and gas supply chain leveraging Bayesian belief network," International Journal of Production Economics, Elsevier, vol. 235(C).
    10. Maru, H. & Haileslassie, Amare & Zeleke, T. & Esayas, B., 2021. "Analysis of smallholders’ livelihood vulnerability to drought across agroecology and farm typology in the Upper Awash Sub-basin, Ethiopia," Papers published in Journals (Open Access), International Water Management Institute, pages 1-13(17):97.
    11. 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.
    12. Husen Maru & Amare Haileslassie & Tesfaye Zeleke & Befikadu Esayas, 2021. "Analysis of Smallholders’ Livelihood Vulnerability to Drought across Agroecology and Farm Typology in the Upper Awash Sub-Basin, Ethiopia," Sustainability, MDPI, vol. 13(17), pages 1-28, August.
    13. Seiford, Lawrence M. & Zhu, Joe, 2002. "Modeling undesirable factors in efficiency evaluation," European Journal of Operational Research, Elsevier, vol. 142(1), pages 16-20, October.
    14. Wade D. Cook & Joe Zhu, 2007. "Data Irregularities And Structural Complexities In Dea," Springer Books, in: Joe Zhu & Wade D. Cook (ed.), Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis, chapter 0, pages 1-11, Springer.
    15. Isaac Olushola Ogunkola & Yusuff Adebayo Adebisi & Uchenna Frank Imo & Goodness Ogeyi Odey & Ekpereonne Esu & Don Eliseo Lucero‐Prisno, 2020. "Rural communities in Africa should not be forgotten in responses to COVID‐19," International Journal of Health Planning and Management, Wiley Blackwell, vol. 35(6), pages 1302-1305, November.
    16. EMROUZNEJAD, Ali & TAVANA, Madjid & HATAMI-MARBINI, Adel, 2014. "The state of the art in fuzzy data envelopment analysis," LIDAM Reprints CORE 2543, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    17. Zhongsheng Hua & Yiwen Bian, 2007. "DEA with Undesirable Factors," Springer Books, in: Joe Zhu & Wade D. Cook (ed.), Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis, chapter 0, pages 103-121, Springer.
    18. William W. Cooper & Lawrence M. Seiford & Kaoru Tone, 2006. "Introduction to Data Envelopment Analysis and Its Uses," Springer Books, Springer, number 978-0-387-29122-2, December.
    19. Yasar A. Ozcan, 2014. "Health Care Benchmarking and Performance Evaluation," International Series in Operations Research and Management Science, Springer, edition 2, number 978-1-4899-7472-3, March.
    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. Wai‐Peng Wong & Qiang Deng & Ming-Lang Tseng & Loo‐Hay Lee & Chee‐Wooi Hooy, 2014. "A Stochastic Setting To Bank Financial Performance For Refining Efficiency Estimates," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 21(4), pages 225-245, October.
    2. W. Liu & W. Meng & X. Li & D. Zhang, 2010. "DEA models with undesirable inputs and outputs," Annals of Operations Research, Springer, vol. 173(1), pages 177-194, January.
    3. Djula Borozan, 2021. "Technical Efficiency and Productivity Change in the European Union with Undesirable Output Considered," Energies, MDPI, vol. 14(16), pages 1-15, August.
    4. Zhou, P. & Ang, B.W. & Poh, K.L., 2008. "A survey of data envelopment analysis in energy and environmental studies," European Journal of Operational Research, Elsevier, vol. 189(1), pages 1-18, August.
    5. Nikolaos Oikonomou & Yannis Tountas & Argiris Mariolis & Kyriakos Souliotis & Kostas Athanasakis & John Kyriopoulos, 2016. "Measuring the efficiency of the Greek rural primary health care using a restricted DEA model; the case of southern and western Greece," Health Care Management Science, Springer, vol. 19(4), pages 313-325, December.
    6. Sebastian Kohl & Jan Schoenfelder & Andreas Fügener & Jens O. Brunner, 2019. "The use of Data Envelopment Analysis (DEA) in healthcare with a focus on hospitals," Health Care Management Science, Springer, vol. 22(2), pages 245-286, June.
    7. Rita Bastião & Nuno de Sousa Pereira, 2020. "Performance in the Delivery of Primary Health Care Services: A Longitudinal Analysis," CEF.UP Working Papers 2002, Universidade do Porto, Faculdade de Economia do Porto.
    8. Chien-Ming Chen & Magali A. Delmas, 2012. "Measuring Eco-Inefficiency: A New Frontier Approach," Operations Research, INFORMS, vol. 60(5), pages 1064-1079, October.
    9. Cook, Wade D. & Seiford, Larry M., 2009. "Data envelopment analysis (DEA) - Thirty years on," European Journal of Operational Research, Elsevier, vol. 192(1), pages 1-17, January.
    10. Zilla Sinuany-Stern & Simona Cohen-Kadosh & Lea Friedman, 2016. "The relationship between the efficiency of orthopedic wards and the socio-economic status of their patients," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 24(4), pages 853-876, December.
    11. Cordero, José Manuel & Alonso-Morán, Edurne & Nuño-Solinis, Roberto & Orueta, Juan F. & Arce, Regina Sauto, 2015. "Efficiency assessment of primary care providers: A conditional nonparametric approach," European Journal of Operational Research, Elsevier, vol. 240(1), pages 235-244.
    12. Zanella, Andreia & Camanho, Ana S. & Dias, Teresa G., 2015. "Undesirable outputs and weighting schemes in composite indicators based on data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 245(2), pages 517-530.
    13. Alessandra Cepparulo & Gilles Mourre, 2020. "How and How Much? The Growth-Friendliness of Public Spending through the Lens," European Economy - Discussion Papers 132, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    14. Cherchye, Laurens & Rock, Bram De & Walheer, Barnabé, 2015. "Multi-output efficiency with good and bad outputs," European Journal of Operational Research, Elsevier, vol. 240(3), pages 872-881.
    15. Dario Maradin & Bojana Olgić Draženović & Saša Čegar, 2023. "The Efficiency of Offshore Wind Energy Companies in the European Countries: A DEA Approach," Energies, MDPI, vol. 16(9), pages 1-16, April.
    16. Taleb, Mushtaq & Khalid, Ruzelan & Ramli, Razamin & Ghasemi, Mohammad Reza & Ignatius, Joshua, 2022. "An integrated bi-objective data envelopment analysis model for measuring returns to scale," European Journal of Operational Research, Elsevier, vol. 296(3), pages 967-979.
    17. Angeliki Flokou & Vassilis Aletras & Dimitris Niakas, 2017. "Decomposition of potential efficiency gains from hospital mergers in Greece," Health Care Management Science, Springer, vol. 20(4), pages 467-484, December.
    18. Kao, Chiang, 2020. "Measuring efficiency in a general production possibility set allowing for negative data," European Journal of Operational Research, Elsevier, vol. 282(3), pages 980-988.
    19. Hsiao-Yen Mao & Wen-Min Lu & Hsin-Yen Shieh, 2023. "Exploring the Influence of Environmental Investment on Multinational Enterprises’ Performance from the Sustainability and Marketability Efficiency Perspectives," Sustainability, MDPI, vol. 15(10), pages 1-23, May.
    20. Matthias Staessens & Pieter Jan Kerstens & Johan Bruneel & Laurens Cherchye, 2019. "Data Envelopment Analysis and Social Enterprises: Analysing Performance, Strategic Orientation and Mission Drift," Journal of Business Ethics, Springer, vol. 159(2), pages 325-341, October.

    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:13:y:2021:i:21:p:11934-:d:667009. 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.