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A dynamic multi-stage slacks-based measure data envelopment analysis model with knowledge accumulation and technological evolution

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  • Santos Arteaga, Francisco J.
  • Tavana, Madjid
  • Di Caprio, Debora
  • Toloo, Mehdi

Abstract

Dynamic data envelopment analysis (DEA) models are built on the idea that single period optimization is not fully appropriate to evaluate the performance of decision making units (DMUs) through time. As a result, these models provide a suitable framework to incorporate the different cumulative processes determining the evolution and strategic behavior of firms in the economics and business literatures. In the current paper, we incorporate two distinct complementary types of sequentially cumulative processes within a dynamic slacks-based measure DEA model. In particular, human capital and knowledge, constituting fundamental intangible inputs, exhibit a cumulative effect that goes beyond the corresponding factor endowment per period. At the same time, carry-over activities between consecutive periods will be used to define the pervasive effect that technology and infrastructures have on the productive capacity and efficiency of DMUs. The resulting dynamic DEA model accounts for the evolution of the knowledge accumulation and technological development processes of DMUs when evaluating both their overall and per period efficiency. Several numerical examples and a case study are included to demonstrate the applicability and efficacy of the proposed method.

Suggested Citation

  • Santos Arteaga, Francisco J. & Tavana, Madjid & Di Caprio, Debora & Toloo, Mehdi, 2019. "A dynamic multi-stage slacks-based measure data envelopment analysis model with knowledge accumulation and technological evolution," European Journal of Operational Research, Elsevier, vol. 278(2), pages 448-462.
    • Handle: RePEc:eee:ejores:v:278:y:2019:i:2:p:448-462
    DOI: 10.1016/j.ejor.2018.09.008
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    2. Svetlana Ratner & Konstantin Gomonov & Svetlana Revinova, 2023. "Public Funding for Energy Innovation and Decarbonization Goals: A Coherence Challenge," International Journal of Energy Economics and Policy, Econjournals, vol. 13(4), pages 40-45, July.
    3. Aistleitner, Matthias & Gräbner, Claudius & Hornykewycz, Anna, 2021. "Theory and empirics of capability accumulation: Implications for macroeconomic modeling," Research Policy, Elsevier, vol. 50(6).
    4. Francisco J. Santos-Arteaga & Debora Di Caprio & Madjid Tavana, 2024. "Information and Communication Technologies and Labor Productivity: A Dynamic Slacks-Based Data Envelopment Analysis," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(3), pages 14076-14102, September.
    5. Francisco Javier Santos Arteaga & Debora Di Caprio & David Cucchiari & Josep M Campistol & Federico Oppenheimer & Fritz Diekmann & Ignacio Revuelta, 2021. "Modeling patients as decision making units: evaluating the efficiency of kidney transplantation through data envelopment analysis," Health Care Management Science, Springer, vol. 24(1), pages 55-71, March.
    6. Svetlana V. Ratner & Artem M. Shaposhnikov & Andrey V. Lychev, 2023. "Network DEA and Its Applications (2017–2022): A Systematic Literature Review," Mathematics, MDPI, vol. 11(9), pages 1-24, May.
    7. Alizadeh, Reza & Gharizadeh Beiragh, Ramin & Soltanisehat, Leili & Soltanzadeh, Elham & Lund, Peter D., 2020. "Performance evaluation of complex electricity generation systems: A dynamic network-based data envelopment analysis approach," Energy Economics, Elsevier, vol. 91(C).
    8. Linhao Zhao & YunQian Zhang & Muhammad Sadiq & Vu Minh Hieu & Thanh Quang Ngo, 2023. "Testing green fiscal policies for green investment, innovation and green productivity amid the COVID-19 era," Economic Change and Restructuring, Springer, vol. 56(5), pages 2943-2964, October.
    9. Chunhua Chen & Jianwei Ren & Lijun Tang & Haohua Liu, 2020. "Additive integer-valued data envelopment analysis with missing data: A multi-criteria evaluation approach," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-20, June.

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