Application of a hybrid method in disaster prevention and relief evaluation

    Chun Chu Liu Affiliation
    ; Tse Yu Wang Affiliation


The purpose of this study is to propose a hybrid method for disaster prevention and relief (DPR) evaluation for Taiwan. Through the hybrid method and evaluation results, the central and local governments of Taiwan could continuously improve and strengthen their DPR system. The main structure of the evaluation is based on the balanced scorecard (BSC), and 15 indicators are gathered from the literature on related issues. These indicators are further analyzed by data envelopment analysis (DEA) and the Malmquist productivity index (MPI) to assess the DPR efficiency of 13 administrative regions in Taiwan. The analysis shows that the DPR system in Taiwan might be improved in Yunlin and Hsinchu City, two administrative regions analyzed during the three stages and time frame studied. The indicators that most significantly affect DPR efficiency are the average number of people served by each government employee or teacher (L1), the supervision score of the Department of Medical Services (DMS) of the Ministry of Health and Welfare (I4), the number of licensed medical practitioners per 10,000 people (C1) and the number of social welfare workers per 10,000 people (C2). These indicators also reflect Taiwan's current shortages in DPR-related and medical personnel.

First published online 23 August 2019

Keyword : disaster prevention and relief, balanced scorecard, data envelopment analysis, Malmquist productivity index

How to Cite
Liu, C. C., & Wang, T. Y. (2019). Application of a hybrid method in disaster prevention and relief evaluation. Technological and Economic Development of Economy, 25(6), 1097-1122.
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Aug 23, 2019
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Ahn, Y. H., & Min, H. (2014). Evaluating the multi-period operating efficiency of international airports using data envelopment analysis and the Malmquist productivity index. Journal of Air Transport Management, 39, 12-22.

Amado, C. A. F., Santos, S. P., & Marques, P. M. (2012). Integrating the data envelopment analysis and the balanced scorecard approaches for enhanced performance assessment. Omega, 40(3), 390-403.

Aryanezhad, M. B., Najafi, E., & Farkoosh, S. B. (2011). A BSC–DEA approach to measure the relative efficiency of service industry, A case study of banking sector. International Journal of Industrial Engineering Computations, 2(2), 273-282.

Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078-1092.

Basso, A., Casarin, F., & Funari, S. (2018). How well is the museum performing? A joint use of DEA and BSC to measure the performance of museums. Omega, 81, 67-84.

Bazrkar, A., & Iranzadeh, S. (2017). Choosing a strategic process in order to apply in Lean Six Sigma methodology for improving its performance using integrative approaches of BSC and DEA. Journal of Business and Retail Management Research, 11(4), 114-123.

Caves, D. W., Christensen, L. R., & Diewert, W. E. (1982). The economic theory of index numbers and the measurement of input, output, and productivity. Econometrica, 50(6), 1393-1414.

Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444.

Charnes, A., & Neralic, L. (1990). Sensitivity analysis of the additive model in data envelopment analysis. European Journal of Operational Research, 48(3), 332-341.

Cheng, H. T., & Chang, H. S. (2018). A spatial DEA-based framework for analyzing the effectiveness of disaster risk reduction policy implementation: a case study of earthquake-oriented urban renewal policy in Yongkang, Taiwan. Sustainability, 10(6), 1-18.

Cook, W. D., & Seiford, L. M. (2009). Data envelopment analysis (DEA) − Thirty years on. European Journal of Operational Research, 192(1), 1-17.

De Nicola, A., Gitto, S., & Mancuso, P. (2013). Airport quality and productivity changes, A Malmquist index decomposition assessment. Transportation Research Part E, Logistics and Transportation Review, 58, 67-75.

Dilley, M., Chen, R. S., Deichmann, U., Lerner-Lam, A. L., & Arnold, M. (2005). Natural disaster hotspots a global risk analysis (Disaster risk management series). Washington, DC: World Bank. Retrieved from PER0Na101official0use0only1.pdf

Dolasinski, M. J., Reborts, C., & Zheng, T. (2019). Measuring hotel channel mix: a Dea-Bsc model. Journal of Hospitality & Tourism Research, 43(2), 188-209.

Egilmez, G., & McAvoy, D. (2013). Benchmarking road safety of U.S. states, A DEA–based Malmquist productivity index approach. Accident Analysis and Prevention, 53, 55-64.

Emrouznejad, A., & Yang, G. L. (2018). A survey and analysis of the first 40 years of scholarly literature in DEA: 1978-2016. Socio-Economic Planning Sciences, 61, 4-8.

Eilat, H., Golany, B., & Shtub, A. (2008). R&D project evaluation: an integrated DEA and balanced scorecard approach. Omega, 36(5), 598-912.

Falavigna, G., Ippoliti, R., & Ramello, G. B. (2018). DEA-based Malmquist productivity indexes for understanding courts reform. Socio–Economic Planning Science, 62, 31-43.

Färe, R., Grosskopf, S., Norris, M., & Zhang, Z. (1994). Productivity growth, technical progress, and efficiency change in industrialized countries. American Economic Review, 84(1), 66-83.

Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society. Series A (General), 120(3), 253-290.

Fletcher, H. D., & Brannigan, S. D. (2004). Managing for value: developing a performance measurement system integrating economic value added and the balanced scorecard in strategic planning. Journal of Business Strategies, 21(1), 1-17.

Golany, B., & Roll, Y. (1989). An application procedure for DEA. Omega, 17(3), 237-250.

Haghighi, S. M., Torabi, S. A., & Ghasemi, R. (2016). An integrated approach for performance evaluation in sustainable supply chain networks (with a case study). Journal of Cleaner Production, 137, 579-597.

Hu, B., Leopold-Wildburger, U., & Strohhecker, J. (2017). Strategy map concepts in a balanced scorecard cockpit improve performance. European Journal of Operational Research, 258(2), 664-676.

Huang, D., Zhang, R., Huo, Z., Mao, F., Youhao, E., & Zheng, W. (2012). An assessment of multidimensional flood vulnerability at the provincial scale in China based on the DEA method. Natural Hazards, 64(2), 1575-1586.

Huang, J., Liu, Y., & Ma, L. (2011). Assessment of regional vulnerability to natural hazards in China using a DEA model. International Journal of Disaster Risk Science, 2(2), 41-48.

Kádárová, J., Durkáčová, M., Teplická, K., & Kádár, G. (2015). The proposal of an innovative integrated BSC – DEA model. Procedia Economics and Finance, 23, 1503-1508.

Kamarudin, F., Hue, C. Z., Sufían, F., & Anwar, N. A. M. (2017). Does productivity of Islamic banks endure progress or regress?: empirical evidence using data envelopment analysis based Malmquist Productivity Index. Humanomics, 33(1), 84-118.

Kaplan, R. S., & Norton, D. P. (1996). The balanced scorecard, translating strategy into action (1st ed.). Boston, MA: Harvard Business School Press.

Kaplan, R. S., & Norton, D. P. (2000). The strategy–focused organization, how balanced scorecard companies thrive in the new business environment (1st ed.). Boston, MA: Harvard Business School Press.

Kaplan, R. S., & Norton, D. P. (2004). Strategy maps, converting intangible assets into tangible outcomes (1st ed.). Boston, MA: Harvard Business School Press.

Kaplan, R. S., & Norton, D. P. (2006). Alignment, using the balanced scorecard to create corporate synergies. Boston, MA: Harvard Business School Press.

Kaplan, R. S., & Norton, D. P. (1992). The balanced scorecard – masures that drive performance. Harvard Business Review, 70(1), 71-79.

Kartalis, N., Velentzas, J., & Broni, G. (2013). Balance scorecard and performance measurement in a Greek industry. Procedia Economics and Finance, 5, 413-422.

Khalili, J., & Alinezhad, A. (2018). Performance evaluation in green supply chain using BSC, DEA and data mining. International Journal of Supply and Operations Management, 5(2), 182-191.

Lotfi, F. H., Sadjadi, S. J., Khaki, A., & Najafi, E. (2010). A combined interval net DEA and BSC for evaluating organizational efficiency. Applied Mathematical Sciences, 4(40), 1975-1999.

Lu, M. T., Hsu, C. C., Liou, J. J. H., & Lo, H. W. (2018). A hybrid MCDM and sustainability-balanced scorecard model to establish sustainable performance evaluation for international airports. Journal of Air Transport Management, 71, 9-19.

Malmquist, S. (1953). Index numbers and indifference surfaces. Trabajos de Estadistica, 4(2), 209-242.

Maroto, A., & Zofí, J. L. (2016). Accessibility gains and road infrastructure in Spain: a productivity approach based on the Malmquist index. Journal of Transport Geography, 52, 143-152.

Mehralian, G., Nazari, J. A., Nooriparto, G., & Rasekh, H. R. (2017). TQM and organizational performance using the balanced scorecard approach. International Journal of Productivity and Performance Management, 66(1), 111-125.

Milis, K., & Mercken, R. (2004). The use of the balanced scorecard for the evaluation of information and communication technology projects. International Journal of Project Management, 22(2), 87-97.

Moe, T. L., Gehbauer, F., Senitz, S., & Mueller, M. (2007). Balanced scorecard for natural disaster management projects. Disaster Prevention and Management, 16(5), 785-806.

Mostafaeipour, A., Qolipour, M., & Mohammadi, K. (2016). Evaluation of installing photovoltaic plants using a hybrid approach for Khuzestan province, Iran. Renewable and Sustainable Energy Reviews, 60, 60-74.

Neralic, L. (1997). Sensitivity in data envelopment analysis for arbitrary perturbations of data. Glasnik Matematicki, 32(2), 315-335.

Örkcü, H. H., Balıkçı, C., Dogan, M. I., & Genç, A. (2016). An evaluation of the operational efficiency of turkish airports using data envelopment analysis and the Malmquist productivity index: 2009–2014 case 2009 to 2014. Transport Policy, 48, 92-104.

Paramanik, S. S., & Kar, S. K. (2013). Green technology performance measurement using BSC–DEA approach. IUP Journal of Knowledge Management, 11(4), 20-35.

Qazi, A. Q., & Yulin, Z. (2012). Productivity measurement of hi–tech industry of China Malmquist productivity index – DEA approach. Procedia Economics and Finance, 1, 330-336.

Qolipour, M., Mostafaeipour, A., Shamshirband, S., Alavi, O., Goudarzi, H., & Petković, D. (2016). Evaluation of wind power generation potential using a three hybrid approach for households in Ardebil Province, Iran. Energy Conversion and Management, 118, 295-305.

Sadeghani, M., Mollaverdi, N., Shirouyehzad, H., & Jafarpour, E. (2013). Analysis of the efficiency R & D projects base on BSC–DEA approach with restrictions on weight of inputs and outputs. International Journal of Economy, Management and Social Sciences, 2(10), 775-779.

Saein, A. F., & Saen, R. F. (2012). Assessment of the site effect vulnerability within urban regions by data envelopment analysis: a case study in Iran. Computers & Geosciences, 48, 280-288.

Shafiee, M., Lotfi, F. H., & Saleh, H. (2014). Supply chain performance evaluation with data envelopment analysis and balanced scorecard approach. Applied Mathematical Modelling, 38(21-22), 50925112.

Shen, Y. C., Chen, P. S., & Wang, C. H. (2016). A study of enterprise resource planning (ERP) system performance measurement using the quantitative balanced scorecard approach. Computers in Industry, 75, 127-139.

Shephard, R. W. (1970). Theory of cost and production functions. Princeton, NJ: Princeton University Press.

Singh, S., Olugu, E. U., Musa, S. N., & Mahat, A. B. (2018). Fuzzy-based sustainability evaluation method for manufacturing SMEs using balanced scorecard framework. Journal of Intelligent Manufacturing, 29(1), 1-18.

Sun, Y., Yu, X., Tan, Z., Xu, X., & Yun, Q. (2017). Efficiency evaluation of operation analysis systems based on dynamic data envelope analysis models from a Big Data Perspective. Applied Science, 7(6), 1-14.

Tan, Y., Zhang, Y., & Khodaverdi, R. (2017). Service performance evaluation using data envelopment analysis and balance scorecard approach: an application to automotive industry. Annals of Operations Research, 248(1-2), 449-470.

Üstün, A. K. (2016). Evaluating İstanbul’s disaster resilience capacity by data envelopment analysis. Natural Hazards, 80(3), 1603-1623.

Üstün, A. K., & Barbarosoğlu, G. (2015). Performance evaluation of Turkish disaster relief management system in 1999 earthquakes using data envelopment analysis. Natural Hazards, 75(2), 1977-1996.

Wang, C. H., & Chien, Y. W. (2016). Combining balanced scorecard with data envelopment analysis to conduct performance diagnosis for Taiwanese LED manufacturers. International Journal of Production Research, 54(17), 5169-5181.

Wei, Y. M., Fan, Y., Lu, C., & Tsai, H. T. (2004). The assessment of vulnerability to natural disasters in China by using the DEA method. Environmental Impact Assessment Review, 24(4), 427-439.

Yuan, X. C., Wang, Q., Wang, K., Wang, B., Jin, J. L., & Wei, Y. M. (2015). China’s regional vulnerability to drought and its mitigation strategies under climate change: data envelopment analysis and analytic hierarchy process integrated approach. Mitigation and Adaptation Strategies for Global Change, 20(3), 341-359.

Zou, L. L., & Wei, Y. M. (2009). Impact assessment using DEA of coastal hazards on social-economy in Southeast Asia. Natural Hazards, 48(2), 167-189.