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Using DEA and survival analysis for measuring performance of branches in New Zealand's Accident Compensation Corporation

Author

Listed:
  • M Meimand

    (New Zealand Treasury)

  • R Y Cavana

    (Victoria University of Wellington)

  • R Laking

    (Victoria University of Wellington)

Abstract

This paper describes an approach to solving the problem of assessing relative branch performance in the Accident Compensation Corporation (ACC), the New Zealand state-owned, no-fault, personal injury compensation insurance company. The methodology described in this paper is innovative in that it assesses relative performance via a modified data envelopment analysis (DEA) process. Performance is assessed and can be examined in terms that are meaningful to business goals. Performance evaluation has generally aligned well with management preconceptions of performance. DEA results are clustered to find common performance themes that are reviewed for business acceptability and the successful themes are used to measure performance against a Farrell frontier. The methodology is applicable to other situations where there are multiple instances of a unit performing the same or similar functions. Technical analysis as used in this paper is based on survival analysis, DEA, clustering and knowledge of the organisation's business operations and objectives.

Suggested Citation

  • M Meimand & R Y Cavana & R Laking, 2002. "Using DEA and survival analysis for measuring performance of branches in New Zealand's Accident Compensation Corporation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(3), pages 303-313, March.
    • Handle: RePEc:pal:jorsoc:v:53:y:2002:i:3:d:10.1057_palgrave.jors.2601291
    DOI: 10.1057/palgrave.jors.2601291
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    Citations

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    Cited by:

    1. Sakouvogui Kekoura & Shaik Saleem & Addey Kwame Asiam, 2020. "Cluster-Adjusted DEA Efficiency in the presence of Heterogeneity: An Application to Banking Sector," Open Economics, De Gruyter, vol. 3(1), pages 50-69, January.
    2. R. Meenakumari & N. Kamaraj, 2008. "Measurement of Relative Efficiency of State Owned Electric Utilities in INDIA Using Data Envelopment Analysis," Modern Applied Science, Canadian Center of Science and Education, vol. 2(5), pages 1-61, September.
    3. Kaffash, Sepideh & Azizi, Roza & Huang, Ying & Zhu, Joe, 2020. "A survey of data envelopment analysis applications in the insurance industry 1993–2018," European Journal of Operational Research, Elsevier, vol. 284(3), pages 801-813.
    4. Basri Savitha & Subrato Banerjee & Ankitha Shetty, 2019. "Product diversification versus technical efficiency of conglomerate life microinsurance companies: evidence from India," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 44(3), pages 527-547, July.
    5. Chiang Ku Fan, 2012. "Compare bancassurance sales with an insurer's own team in Taiwan," International Journal of Business Performance Management, Inderscience Enterprises Ltd, vol. 13(3/4), pages 274-293.
    6. George Halkos & Mike G. Tsionas, 2019. "Accounting for Heterogeneity in Environmental Performance Using Data Envelopment Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 54(3), pages 1005-1025, October.
    7. Ankitha Shetty & Savitha Basri, 2020. "Assessing the Technical Efficiency of Traditional and Corporate Agents in Indian Life Insurance Industry: Slack-based Data Envelopment Analysis Approach," Global Business Review, International Management Institute, vol. 21(2), pages 490-506, April.
    8. Pang-Ru Chang & Jin-Lung Peng & Chiang Ku Fan, 2011. "A Comparison of Bancassurance and Traditional Insurer Sales Channels," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 36(1), pages 76-93, January.
    9. Fuad Aleskerov & Vsevolod Petrushchenko, 2016. "DEA by sequential exclusion of alternatives in heterogeneous samples," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(01), pages 5-22, January.
    10. Samoilenko, Sergey & Osei-Bryson, Kweku-Muata, 2010. "Determining sources of relative inefficiency in heterogeneous samples: Methodology using Cluster Analysis, DEA and Neural Networks," European Journal of Operational Research, Elsevier, vol. 206(2), pages 479-487, October.
    11. Tsionas, Mike G., 2023. "Clustering and meta-envelopment in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 304(2), pages 763-778.

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