Based on the performance change intensity Si and the RTS status, 25 sample insurance companies are distributed in four quadrants, reflecting the differences in the resili...
Abstract:
In the rapidly changing business world, insurance companies should observe long-term operational performance and focus on its resilience. Existing studies rarely investig...Show MoreMetadata
Abstract:
In the rapidly changing business world, insurance companies should observe long-term operational performance and focus on its resilience. Existing studies rarely investigate the resilience issue of performance under the context of the insurance sector. Hence, this study develops a dynamic data envelopment analysis (DEA) model to measure the operational performance of insurance companies by considering multiple periods. Different from the existing works adopting traditional static DEA, our work develops a dynamic DEA model to estimate performance by incorporating multiple periods via a common objective function. Besides, the measurement for estimating the resilience of performance is further developed. The proposed approach in this study is applied to 25 Chinese insurance companies for three years from 2017 to 2019. The results reveal that most insurance companies’ performances still have improvement potential. And there exists a distinct difference in performance between property and casualty insurance companies and life insurance companies during the observed period. Additionally, the resilience of the performance of most inefficient insurance companies is at a weak level. Finally, some management implications are provided for improving operational performance.
Based on the performance change intensity Si and the RTS status, 25 sample insurance companies are distributed in four quadrants, reflecting the differences in the resili...
Published in: IEEE Access ( Volume: 11)
Funding Agency:
References is not available for this document.
Contents Select All
1.
W. Wise, “Life insurance company efficiency: Best method and proxies,” Insurance Markets Companies, vol. 9, no. 1, pp. 10–21, 2018.
2.
National Bureau of Statistics of China, National Data. Accessed: Mar. 10, 2023. [Online]. Available: https://data.stats.gov.cn/english/easyquery.htm?cn=C01
3.
W.-M. Lu, W.-K. Wang, and Q. L. Kweh, “Intellectual capital and performance in the Chinese life insurance industry,” Omega, vol. 42, no. 1, pp. 65–74, Jan. 2014.
4.
C. Kao and S.-N. Hwang, “Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan,” Eur. J. Oper. Res., vol. 185, no. 1, pp. 418–429, Feb. 2008.
5.
Y.-S. Chen, C.-K. Lin, Y.-S. Lin, S.-F. Chen, and H.-H. Tsao, “Identification of potential valid clients for a sustainable insurance policy using an advanced mixed classification model,” Sustainability, vol. 14, no. 7, p. 3964, Mar. 2022.
6.
J. D. Cummins, M. Rubio-Misas, and H. Zi, “The effect of organizational structure on efficiency: Evidence from the Spanish insurance industry,” J. Banking Finance, vol. 28, no. 12, pp. 3113–3150, Dec. 2004.
7.
S. Yao, Z. Han, and G. Feng, “On technical efficiency of China’s insurance industry after WTO accession,” China Econ. Rev., vol. 18, no. 1, pp. 66–86, Jan. 2007.
8.
M. Nourani, E. S. Devadason, and V. Chandran, “Measuring technical efficiency of insurance companies using dynamic network DEA: An intermediation approach,” Technol. Econ. Develop. Economy, vol. 24, no. 5, pp. 1909–1940, Oct. 2018.
9.
S. Mandal and S. Ghosh Dastidar, “A DEA-investigation of efficiency of the Indian general insurance during recession,” J. Adv. Manage. Res., vol. 11, no. 1, pp. 115–136, Apr. 2014.
10.
C. Biener and M. Eling, “The performance of microinsurance programs: A data envelopment analysis,” J. Risk Insurance, vol. 78, no. 1, pp. 83–115, Mar. 2011.
11.
H. Zhang, J. You, X. Haiyirete, and T. Zhang, “Measuring logistics efficiency in China considering technology heterogeneity and carbon emission through a meta-frontier model,” Sustainability, vol. 12, no. 19, p. 8157, Oct. 2020.
12.
M. Eling and M. Luhnen, “Efficiency in the international insurance industry: A cross-country comparison,” J. Banking Finance, vol. 34, no. 7, pp. 1497–1509, Jul. 2010.
13.
S. Kaffash, R. Azizi, Y. Huang, and J. Zhu, “A survey of data envelopment analysis applications in the insurance industry 1993–2018,” Eur. J. Oper. Res., vol. 284, no. 3, pp. 801–813, Aug. 2020.
14.
T. Zhao, R. Pei, and J. Pan, “The evolution and determinants of Chinese property insurance companies’ profitability: A DEA-based perspective,” J. Manage. Sci. Eng., vol. 6, no. 4, pp. 449–466, Dec. 2021.
15.
K. Tone, Q. L. Kweh, W.-M. Lu, and I. W. K. Ting, “Modeling investments in the dynamic network performance of insurance companies,” Omega, vol. 88, pp. 237–247, Oct. 2019.
16.
H. Omrani, A. Emrouznejad, M. Shamsi, and P. Fahimi, “Evaluation of insurance companies considering uncertainty: A multi-objective network data envelopment analysis model with negative data and undesirable outputs,” Socio-Econ. Planning Sci., vol. 82, Aug. 2022, Art. no. 101306.
17.
K. Tone and M. Tsutsui, “Dynamic DEA: A slacks-based measure approach,” Omega, vol. 38, nos. 3–4, pp. 145–156, Jun. 2010.
18.
Q. L. Kweh, W.-M. Lu, and W.-K. Wang, “Dynamic efficiency: Intellectual capital in the Chinese non-life insurance firms,” J. Knowl. Manage., vol. 18, no. 5, pp. 937–951, Sep. 2014.
19.
S. Hosseini, K. Barker, and J. E. Ramirez-Marquez, “A review of definitions and measures of system resilience,” Rel. Eng. Syst. Saf., vol. 145, pp. 47–61, Jan. 2016.
20.
J. Chu, J. Wu, C. Chu, and M. Liu, “A new DEA common-weight multi-criteria decision-making approach for technology selection,” Int. J. Prod. Res., vol. 58, no. 12, pp. 3686–3700, Jun. 2020.
21.
G. Li, T. Luo, and Y. Song, “Climate change mitigation efficiency of electric vehicle charging infrastructure in China: From the perspective of energy transition and circular economy,” Resour. Conserv. Recy., vol. 179, Apr. 2022, Art. no. 106048.
22.
R. Färe, S. Grosskopf, B. Lindgren, and P. Roos, “Productivity changes in Swedish pharamacies 1980–1989: A non-parametric Malmquist approach,” J. Prod. Anal., vol. 3, nos. 1–2, pp. 85–101, 1992.
23.
A. Charnes, C. T. Clark, W. W. Cooper, and B. Golany, “A developmental study of data envelopment analysis in measuring the efficiency of maintenance units in the US air forces,” Ann. Oper. Res., vol. 2, no. 1, pp. 95–112, Dec. 1984.
24.
R. Färe and S. Grosskopf, “Intertemporal production frontiers: With dynamic DEA,” J. Oper. Res. Soc., vol. 48, no. 6, pp. 656–656, Dec. 1997.
25.
K. Tone and M. Tsutsui, “Dynamic DEA with network structure: A slacks-based measure approach,” Omega, vol. 42, no. 1, pp. 124–131, Jan. 2014.
26.
O. Donni and F. Fecher, “Efficiency and productivity of the insurance industry in the OECD countries,” Geneva Papers Risk Insurance Issues Pract., vol. 22, no. 4, pp. 523–535, Oct. 1997.
27.
J. D. Cummins, S. Tennyson, and M. A. Weiss, “Consolidation and efficiency in the U.S. life insurance industry,” J. Banking Finance, vol. 23, nos. 2–4, pp. 325–357, Feb. 1999.
28.
S. Anandarao, S. R. S. Durai, and P. Goyari, “Efficiency decomposition in two-stage data envelopment analysis: An application to life insurance companies in India,” J. Quant. Econ., vol. 17, no. 2, pp. 271–285, Jun. 2019.
29.
Z. Li, X. Si, Z. Ding, X. Li, S. Zheng, Y. Wang, H. Wei, Y. Guo, and W. Zhang, “Measurement and evaluation of the operating efficiency of China’s basic pension insurance: Based on three-stage DEA model,” Risk Manage. Healthcare Policy, vol. Volume 14, pp. 3333–3348, Aug. 2021.
30.
S. A. Siddiqui, “How efficient is Indian health insurance sector: An SBM-DEA study,” Managerial Decis. Econ., vol. 43, no. 4, pp. 950–962, Jun. 2022.