|
[1]
|
Q. An, F. Meng, B. Xiong, Z. Wang and X. Chen, Assessing the relative efficiency of Chinese high-tech industries: A dynamic network data envelopment analysis approach, Annals of Operations Research, (2018), 1–23.
doi: 10.1007/s10479-018-2883-2.
|
|
[2]
|
S. Assani, J. Jiang, A. Assani and F. Yang, Estimating and decomposing most productive scale size in parallel DEA networks with shared inputs: A case of China's Five-Year Plans, preprint, arXiv: 1910.03421.
|
|
[3]
|
S. Assani, J. Jiang, A. Assani and F. Yang, Most productive scale size of China's regional R & D value chain: A mixed structure network, preprint, arXiv: 1910.03805.
|
|
[4]
|
S. Assani, J. Jiang, R. Cao and F. Yang, Most productive scale size decomposition for multi-stage systems in data envelopment analysis, Computers and Industrial Engineering, 120 (2018), 279-287.
doi: 10.1016/j.cie.2018.04.043.
|
|
[5]
|
H. Azizi, S. Kordrostami and A. Amirteimoori, Slacks-based measures of efficiency in imprecise data envelopment analysis: An approach based on data envelopment analysis with double frontiers, Computers and Industrial Engineering, 79 (2015), 42-51.
doi: 10.1016/j.cie.2014.10.019.
|
|
[6]
|
T. Badiezadeh, R. F. Saen and T. Samavati, Assessing sustainability of supply chains by double frontier network DEA: A big data approach, Computers and Operations Research, 98 (2018), 284-290.
doi: 10.1016/j.cor.2017.06.003.
|
|
[7]
|
R. D. Banker, A. Charnes and W. W. Cooper, Some Models for estimating technical and scale inefficiencies in data envelopment analysis, Management Science, 30 (1984), 1078-1092.
doi: 10.1287/mnsc.30.9.1078.
|
|
[8]
|
R. D. Banker and R. M. Thrall, Estimation of returns to scale using data envelopment analysis, European Journal of Operational Research, 62 (1992), 74-84.
doi: 10.1016/0377-2217(92)90178-C.
|
|
[9]
|
N. Becheikh, R. Landry and N. Amara, Lessons from innovation empirical studies in the manufacturing sector: A systematic review of the literature from 1993-2003, Technovation, 26 (2006), 644-664.
doi: 10.1016/j.technovation.2005.06.016.
|
|
[10]
|
R. Blundell, R. Griffith and J. V. Reenen, Market share, market value and innovation in a panel of British manufacturing firms, Review of Economic Studies, 66 (1999), 529-554.
doi: 10.1111/1467-937X.00097.
|
|
[11]
|
N. Capon, J. U. Farley and S. Hoenig, Determinants of financial performance, Management Science, 36 (2011), 1143-1159.
doi: 10.1287/mnsc.36.10.1143.
|
|
[12]
|
A. Charnes and W. W. Cooper, The non-archimedean CCR ratio for efficiency analysis: A rejoinder to Boyd and F$\ddot{a}$re, European Journal of Operational Research, 15 (1984), 333-334.
doi: 10.1016/0377-2217(84)90102-4.
|
|
[13]
|
A. Charnes, W. W. Cooper and E. Rhodes, Measuring the efficiency of decision making units, European Journal of Operational Research, 2 (1978), 429-444.
doi: 10.1016/0377-2217(78)90138-8.
|
|
[14]
|
K. Chen, M. Kou and X. Fu, Evaluation of multi-period regional R & D efficiency: An application of dynamic DEA to China's regional R & D systems, Omega, 74 (2018), 103-114.
doi: 10.1016/j.omega.2017.01.010.
|
|
[15]
|
X. Chen, Z. Liu and Q. Zhu, Performance evaluation of China's high-tech innovation process: Analysis based on the innovation value chain, Technovation, 74-75 (2018), 42-53.
doi: 10.1016/j.technovation.2018.02.009.
|
|
[16]
|
W. W. Cooper, L. M. Seiford and K. Tone, Introduction to Data Envelopment Analysis and Its Uses. In Introduction to Data Envelopment Analysis and Its Uses: With DEA-Solver Software and References, Springer, Boston, 2006.
doi: 10.1007/0-387-29122-9.
|
|
[17]
|
P. Coto-Millán, V. Inglada, X. L. Fernández, L. Inglada-Pérez and M. Á. Pesquera, The "effect procargo" on technical and scale efficiency at airports: The case of Spanish airports (2009-2011), Utilities Policy, 39 (2016), 29-35.
doi: 10.1016/j.jup.2016.01.004.
|
|
[18]
|
D. M. Decarolis and D. L. Deeds, The impact of stocks and flows of organizational knowledge on firm performance: An empirical investigation of the biotechnology industry, Strategic Management Journal, 20 (1999), 953-968.
doi: 10.1002/(SICI)1097-0266(199910)20:10<953::AID-SMJ59>3.0.CO;2-3.
|
|
[19]
|
A. Emrouznejad and G. Yang, A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016, Socio-Economic Planning Sciences, 61 (2018), 4-8.
doi: 10.1016/j.seps.2017.01.008.
|
|
[20]
|
S. B. Graves and N. S. Langowitz, R & D productivity: A global multi-industry comparison, Technological Forecasting and Social Change, 53 (1996), 125-137.
doi: 10.1016/S0040-1625(96)00068-6.
|
|
[21]
|
J. Guan and K. Chen, Measuring the innovation production process: A cross-region empirical study of China's high-tech innovations, Technovation, 30 (2010), 348-358.
doi: 10.1016/j.technovation.2010.02.001.
|
|
[22]
|
B. H. Hall and R. H. Ziedonis, The patent paradox revisited: An empirical study of patenting in the U.S. semiconductor industry, 1979-1995, The RAND Journal of Economics, 32 (2001), 101-101.
doi: 10.2307/2696400.
|
|
[23]
|
M. A. Hitt, R. E. Hoskisson and H. Kim, International diversification: Effects on innovation and firm performance in product-diversified firms, Academy of Management Journal, 40 (1997), 767-798.
doi: 10.2307/256948.
|
|
[24]
|
K. Hosseini and A. Stefaniec, Efficiency assessment of Iran's petroleum refining industry in the presence of unprofitable output: A dynamic two-stage slacks-based measure, Energy, 189 (2019), 116112.
doi: 10.1016/j.energy.2019.116112.
|
|
[25]
|
J. L. Jiang, E. P. Chew, L. H. Lee and Z. Sun, DEA based on strongly efficient and inefficient frontiers and its application on port efficiency measurement, OR Spectrum, 34 (2012), 943-969.
doi: 10.1007/s00291-011-0263-2.
|
|
[26]
|
C. Kao and S. N. Hwang, Decomposition of technical and scale efficiencies in two-stage production systems, European Journal of Operational Research, 211 (2011), 515-519.
doi: 10.1016/j.ejor.2011.01.010.
|
|
[27]
|
C. Kao and S. N. Hwang, Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan, European Journal of Operational Research, 185 (2008), 418-429.
doi: 10.1016/j.ejor.2006.11.041.
|
|
[28]
|
C. Kao and S. N. Hwang, Scale efficiency measurement in two-stage production systems, International Series in Operations Research and Management Science, 208 (2014), 119-135.
doi: 10.1007/978-1-4899-8068-7_6.
|
|
[29]
|
P. Khoshnevis and P. Teirlinck, Performance evaluation of R & D active firms, Socio-Economic Planning Sciences, 61 (2018), 16-28.
doi: 10.1016/j.seps.2017.01.005.
|
|
[30]
|
S. Lee and H. Lee, Measuring and comparing the R & D performance of government research institutes: A bottom-up data envelopment analysis approach, Journal of Informetrics, 9 (2015), 942-953.
doi: 10.1016/j.joi.2015.10.001.
|
|
[31]
|
J. S. Liu, L. Y. Y. Lu, W. M. Lu and B. J. Y. Lin, A survey of DEA applications, Omega, 41 (2013), 893-902.
doi: 10.1016/j.omega.2012.11.004.
|
|
[32]
|
J. S. Liu and W. M. Lu, DEA and ranking with the network-based approach: A case of R & D performance, Omega, 38 (2010), 453-464.
doi: 10.1016/j.omega.2009.12.002.
|
|
[33]
|
K. Lv, D. Wang and Y. Cheng, Measuring the dynamic performances of innovation production process from the carry-over perspective: An empirical study of China's high-tech industry, Transformations in Business and Economics, 16 (2017), 345-361.
|
|
[34]
|
M. M. Mousavi, J. Ouenniche and K. Tone, A comparative analysis of two-stage distress prediction models, Expert Systems with Applications, 119 (2019), 322-341.
doi: 10.1016/j.eswa.2018.10.053.
|
|
[35]
|
G. P$\acute{e}$rez-L$\acute{o}$pez, D. Prior and J. L. Zafra-G$\acute{o}$mez, Temporal scale efficiency in DEA panel data estimations. An application to the solid waste disposal service in Spain, Omega, 76 (2018), 18-27.
doi: 10.1016/j.omega.2017.03.005.
|
|
[36]
|
$\ddot{U}$. Sa$\check{g}$lam, Assessment of the productive efficiency of large wind farms in the United States: An application of two-stage data envelopment analysis, Energy Conversion and Management, 153 (2017), 188-214.
doi: 10.1016/j.enconman.2017.09.062.
|
|
[37]
|
B. K. Sahoo, J. Zhu, K. Tone and B. M. Klemen, Decomposing technical efficiency and scale elasticity in two-stage network DEA, European Journal of Operational Research, 233 (2014), 584-594.
doi: 10.1016/j.ejor.2013.09.046.
|
|
[38]
|
L. M. Seiford and J. Zhu, Profitability and marketability of the top 55 U.S. commercial banks, Management Science, 45 (1999), 1270-1288.
doi: 10.1287/mnsc.45.9.1270.
|
|
[39]
|
S. R. Seyedalizadeh Ganji, A. Rassafi and D. L. Xu, A double frontier DEA cross efficiency method aggregated by evidential reasoning approach for measuring road safety performance, Measurement, 136 (2019), 668-688.
doi: 10.1016/j.measurement.2018.12.098.
|
|
[40]
|
A. Sterlacchini, Do innovative activities matter to small firms in non R & D intensive industries? An application to export performance, Research Policy, 28 (1999), 819-832.
doi: 10.1016/S0048-7333(99)00023-2.
|
|
[41]
|
T. Sueyoshi and D. Wang, Measuring scale efficiency and returns to scale on large commercial rooftop photovoltaic systems in California, Energy Economics, 65 (2017), 389-398.
doi: 10.1016/j.eneco.2017.04.019.
|
|
[42]
|
S. Thornhill, Knowledge, innovation and firm performance in high and low technology regimes, Journal of Business Venturing, 21 (2006), 687-703.
doi: 10.1016/j.jbusvent.2005.06.001.
|
|
[43]
|
B. Walheer, Scale efficiency for multi-output cost minimizing producers: The case of the US electricity plants, Energy Economics, 70 (2018), 26-36.
doi: 10.1016/j.eneco.2017.12.017.
|
|
[44]
|
C. H. Wang, Y. H. Lu, C. W. Huang and J. Y. Lee, R & D, productivity, and market value: An empirical study from high-technology firms, Omega, 41 (2013), 143-155.
doi: 10.1016/j.omega.2011.12.011.
|
|
[45]
|
Y. M. Wang, K. S. Chin and J. B. Yang, Measuring the performances of decision-making units using geometric average efficiency, Journal of the Operational Research Society, 58 (2007), 929-937.
doi: 10.1057/palgrave.jors.2602205.
|
|
[46]
|
Y. M. Wang and Y. X. Lan, Estimating most productive scale size with double frontiers data envelopment analysis, Economic Modelling, 33 (2013), 182-186.
doi: 10.1016/j.econmod.2013.04.021.
|
|
[47]
|
Y. M. Wang and J. B. Yang, Measuring the performances of decision-making units using interval efficiencies, Journal of Computational and Applied Mathematics, 198 (2007), 253-267.
doi: 10.1016/j.cam.2005.12.025.
|
|
[48]
|
P. F. Wanke and C. P. Barros, Public-private partnerships and scale efficiency in Brazilian ports: Evidence from two-stage DEA analysis, Socio-Economic Planning Sciences, 51 (2015), 13-22.
doi: 10.1016/j.seps.2015.06.002.
|
|
[49]
|
Z. G. Xin and W. Zhen, The technical efficiency of China's wind power list enterprises: An estimation based on DEA method and micro-data, Renewable Energy, 133 (2019), 470-479.
doi: 10.1016/j.renene.2018.10.049.
|
|
[50]
|
V. Zelenyuk, Aggregation of scale efficiency, European Journal of Operational Research, 240 (2015), 269-277.
doi: 10.1016/j.ejor.2014.06.038.
|
|
[51]
|
B. Zhang, Y. Luo and Y. H. Chiu, Efficiency evaluation of China's high-tech industry with a multi-activity network data envelopment analysis approach, Socio-Economic Planning Sciences, 66 (2019), 2-9.
doi: 10.1016/j.seps.2018.07.013.
|
|
[52]
|
W. Zhong, W. Yuan, S. X. Li and Z. Huang, The performance evaluation of regional R & D investments in China: An application of DEA based on the first official China economic census data, Omega, 39 (2011), 447-455.
doi: 10.1016/j.omega.2010.09.004.
|
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