Skip to main content

Time-Series Directional Efficiency for Knowledge Benchmarking in Service Organizations

  • Conference paper
  • First Online:
Trends and Innovations in Information Systems and Technologies (WorldCIST 2020)

Abstract

Data Envelopment Analysis (DEA) is a linear programming tool that indicates benchmarking peers for inefficient service units to become efficient. Nevertheless, for strategic reasons the benchmarking of best practices and knowledge aggregation from efficient competitors is not usual. A time-series adaptation for directional model is proposed in this work as an alternative. The analysis applied to one branch unit of Brazilian Federal Saving Bank allowed an internal benchmarking of efficient periods of which innovative processes, competitive strategies, human resource changes, and specific incentive structures were adopted. This added knowledge provided an advantage to improve the performance of the service unit. In addition, managers to draw the best strategy in each period can use the model on pre-determined goals.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Carpenter, S., Rudge, S.: A self-help approach to knowledge management benchmarking. J. Knowl. Manag. 7(5), 82–95 (2003)

    Article  Google Scholar 

  2. O’Dell, C., Wiig, K., Odem, P.: Benchmarking unveils emerging knowledge management strategies. Benchmark. Int. J. 6(3), 202–211 (1999)

    Article  Google Scholar 

  3. Cepeda-Carrion, I., Martelo-Landroguez, S., Leal-Rodríguez, A.L., Leal-Millán, A.: Critical processes of knowledge management: an approach toward the creation of customer value. Eur. Res. Manag. Bus. Econ. 23, 1–7 (2016). https://doi.org/10.1016/j.iedeen.2016.03.001

    Article  Google Scholar 

  4. Nonaka, I., Konno, N.: The concept of “Ba”: building a foundation for knowledge creation. Calif. Manage. Rev. 40, 40–54 (1998). https://doi.org/10.2307/41165942

    Article  Google Scholar 

  5. Wang, S., Noe, R.A.: Knowledge sharing: a review and directions for future research. Hum. Resour. Manag. Rev. 20, 115–131 (2010). https://doi.org/10.1016/j.hrmr.2009.10.001

    Article  Google Scholar 

  6. Chang, C.L., Lin, T.-C.: The role of organizational culture in the knowledge management process. J. Knowl. Manag. 19, 433–455 (2015). https://doi.org/10.1108/JKM-08-2014-0353

    Article  Google Scholar 

  7. Hooff, B., Huysman, M.: Managing knowledge sharing: emergent and engineering approaches. Inf. Manag. 46, 1–8 (2009). https://doi.org/10.1016/j.im.2008.09.002

    Article  Google Scholar 

  8. Daraio, C., Kerstens, K., Nepomuceno, T., Sickles, R.C.: Empirical surveys of frontier applications: a meta-review. Int. Trans. Oper. Res. 27, 709–738 (2020). https://doi.org/10.1111/itor.12649

    Article  MathSciNet  Google Scholar 

  9. Daraio, C., Kerstens, K.H., Nepomuceno, T.C.C., Sickles, R.: Productivity and efficiency analysis software: an exploratory bibliographical survey of the options. J. Econ. Surv. 33(1), 85–100 (2019)

    Article  Google Scholar 

  10. Calvo-Mora, A., Navarro-García, A., Rey-Moreno, M., Periañez-Cristobal, R.: Excellence management practices, knowledge management and key business results in large organisations and SMEs: a multi-group analysis. Eur. Manag. J. 34, 661–673 (2016). https://doi.org/10.1016/j.emj.2016.06.005

    Article  Google Scholar 

  11. Chambers, R.G., Chung, Y., Färe, R.: Profit, directional distance functions, and Nerlovian efficiency. J. Optim. Theory Appl. 98(2), 351–364 (1998)

    Article  MathSciNet  Google Scholar 

  12. Färe, R., Grosskopf, S.: New directions: efficiency and productivity, vol. 3 (2006)

    Google Scholar 

  13. Daraio, C., Simar, L.: Directional distances and their robust versions: computational and testing issues. Eur. J. Oper. Res. 237(1), 358–369 (2014)

    Article  Google Scholar 

  14. Nepomuceno, T.C., Costa, A.P.C.: Resource allocation with time series DEA applied to Brazilian Federal Saving banks. Econ. Bull. 39(2), 1384–1392 (2019)

    Google Scholar 

  15. Nepomuceno, T.C.C., Daraio, C., Costa, A.P.C.S.: Combining multi-criteria and directional distances to decompose non-compensatory measures of sustainable banking efficiency. Appl. Econ. Lett. 27(4), 329–334 (2020)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Thyago Celso Cavalvante Nepomuceno .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Nepomuceno, T.C.C., de Carvalho, V.D.H., Costa, A.P.C.S. (2020). Time-Series Directional Efficiency for Knowledge Benchmarking in Service Organizations. In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S., Orovic, I., Moreira, F. (eds) Trends and Innovations in Information Systems and Technologies. WorldCIST 2020. Advances in Intelligent Systems and Computing, vol 1159. Springer, Cham. https://doi.org/10.1007/978-3-030-45688-7_34

Download citation

Publish with us

Policies and ethics