Evaluation of Innovation with a Combination of Two-Stage DEA and Cooperative Game Theory

Document Type : Original Article

Authors

1 Master of Industrial Management, Department of Management, Faculty of Administrative Sciences and Economics, Vali Asr University (AJ), Rafsanjan, Iran

2 Assistant Professor, Department of Management, Faculty of Administrative Sciences and Economics, Vali Asr University (AJ), Rafsanjan, Iran

3 Associate Professor, Department of Management, Faculty of Administrative Sciences and Economics, Vali Asr University (AJ), Rafsanjan, Iran

Abstract
Today, one of the main components of the sustainable development of organizations and among the methods of overcoming economic and technological challenges is innovation. Evaluation of the innovation process is an important issue in knowledge-based companies. One of the innovation evaluation methods is two-stage DEA, which allows considering multiple inputs and outputs and the internal structure without any special assumption regarding the production function. In this research, the theory of cooperative games was used to determine the optimal value of intermediate variables. A non-linear cooperative model for a consecutive two-stage structure with surplus input and output and conditions to the variable scale of development and two calculation procedures of variable first stage efficiency and variable second stage efficiency were proposed to solve it. Considering the two stages of research and development and commercialization for the innovation process, the efficiency of the stages and the total of 9 knowledge-based information and communication technology companies was calculated. The results showed that only one company is efficient in both stages and research and development has a greater contribution to the ineffectiveness of innovation in companies compared to commercialization. The results give important insights to managers to identify the source of inefficiency and prioritize resource allocation.

Keywords


[1] Abril, C. & Gimenez-Fernandez, E. M. (2024). Using gamification to overcome innovation process challenges: A literature review and future agenda. Technovation, 133, 103020. DOI:10.1016/j.technovation.2024.103020.
[2] Nandal, N., Kataria, A. & Dhingra, M. (2020). Measuring Innovation: Challenges and Best Practices. International Journal of Advanced Science and Technology, 29, 5, 1275-1285. http://sersc.org/journals/index.php/IJAST/article/view/8157.
[3] Qin, Y., Zhang, P., Deng, X. & Liao, G. (2023). Innovation efficiency evaluation of industrial technology research institute based on three-stage DEA. Expert Systems with Applications, 224. https://doi.org/10.1016/j.eswa.2023.120004. 
[4] An Q., Meng F., Xiong B., Wang Z. & Chen X. (2020). Assessing the relative efficiency of Chinese high-tech industries: a dynamic network data envelopment analysis approach. Annals of Operations Research, 290(1), 707-729. DOI: 10.1007/s10479-018-2883-2.
[5] Zuo, Z., Guo, H., Li, Y., Cheg, J. (2022). A two-stage DEA evaluation of Chinese mining industry technological innovation efficiency and eco-efficiency. Environmental Impact Assessment Review, 94, 106762. https://doi.org/10.1016/j.eiar.2022.106762.
[6] Alinezhad, A., Azar, A. & PourZarandi, M. (2019). Designing a Model for Predicting and Evaluating the Innovation Capacity of Knowledge-based Companies with a Neural-Adaptive Fuzzy Inference System (ANFIS). Public Management Researches, 13, 47, 55-84. [In Persian]
[7] Azar, A., Mohebbi, H., Khadivar, A., & Heydari, A. (2017). A New Mathematical Model to Solve the Assignment Problems Caused by Multiple Heterogeneous Inputs and Outputs. Industrial Management Journal, 9(1), 1-18. [In Persian]
[8] Lin, R. & Li, Z. (2020). Directional distance based diversification super-efficiency DEA models for mutual funds. Omega, 97(C). https://doi.org/10.1016/j.omega.2019.08.003
[9] Mohebbi, H., Azar, A., Heidari, A., & Khadivar, A. (2019). Designing a Mathematical Model for Optimum Assignment in the Two-stage Green Supply Chain using Network Data Envelopment Analysis and Electrical Circuits. Industrial Management Studies, 17(54), 1-23. [In Persian]
[10] Kao C. & Hwang S. N. (2010). Efficiency measurement for network systems: IT impact on firm performance. Decision Support Systems, 48(3):437–446. https://doi.org/10.1016/j.dss.2009.06.002.
[11] Liang, L, Cook W. D. & Zhu J. (2008). DEA Models for Two-Stage Processes: Game Approach and Efficiency Decomposition. Naval Research Logistics, 55. https://doi.org/10.1002/nav.20308.
[12] Halkos, G. E., Tzeremes, N. G. & Kourtzidis, S. A. (2014). A unified classification of two-stage DEA models. Surveys in Operations Research and Management Science, 19(1), 1–16. https://doi.org/10.1016/j.sorms.2013.10.001.
[13] Wang, C., Gopal, R. & Zionts, S. (1997). Use of data envelopment analysis in assessing information technology impact on firm performance. Annals of Operations Research, 73(1), 191–213. https://doi.org/10.1016/j.sorms.2013.10.001.
[14] Seiford, L. M. & Zhu, J. (1999). Profitability and marketability of the top 55. U.S. Commercial Banks. Performance of Financial Institutions, 45(9), 1270–1288. http://dx.doi.org/10.1287/mnsc.45.9.1270.
[15] Chen, Y. & Zhu, J. (2004). Measuring information technology’s indirect impact on firm performance. Information Technology & Management, 5(1), 9–22. https://doi.org/10.1023/B:ITEM.0000008075.43543.97.
[16] Castelli, L, Pesenti, R. & Ukovich, W. (2010). A classification of DEA models when the internal structure of the Decision Making Units is considered. Annals of Operations Research, 173(1), 207–35. https://doi.org/10.1007/s10479-008-0414-2.
[17] Kao, C., Hwang, S. N. (2008). Efficiency decomposition in two-stage data envelopment analysis: an application to non-life insurance companies in Taiwan. European Journal of Operational Research, 185(1), 418–429. https://doi.org/10.1016/j.ejor.2006.11.041.
[18] Chen, Y., Cook, W. D., Li, N. & Zhu, J. (2009). Additive efficiency decomposition in two-stage DEA. European Journal of Operational Research,196(3),1170–1176. https://doi.org/10.1016/j.ejor.2008.05.011.
[19] Liang, L., Yang, F., Cook, W. D. & Zhu, J. (2006). DEA models for supply chain efficiency evaluation. Annals Operations Research, 145(1), 35–49. https://doi.org/10.1007/s10479-006-0026-7.
[20] Chu, J., Wu, J., Chu, C. & Zhang, T. (2019). DEA-based fixed cost allocation in two-stage systems: Leader-follower and satisfaction degree bargaining game approaches. Omega, 94, 102054. https://doi.org/10.1016/j.omega.2019.03.012.
[21] Wang, M., Huang, Y. & Li, D. (2021). Assessing the performance of industrial water resource utilization systems in China based on a two-stage DEA approach with game cross efficiency. Journal of Cleaner Production, 312, 127722. https: //doi.org/10.1016/j.jclepro.2021.127722.
[22] Wu, J., Xu, G., Zhu & Q. Zhang, C. (2021). Two-stage DEA models with fairness concern: Modelling and computational aspects. Omega, 105. https://doi.org/ 10.1016/j.omega.2021.102521.
[23] Yousefi, S., Jahangoshai Rezaee, M. & Solimanpur, M. (2019). Supplier selection and order allocation using two-stage hybrid supply chain model and game-based order price. Operational Research International Journal, 21, 553-588. https://doi.org/10.1007/s12351-019-00456-6.
[24] Fang, L. (2020). Stage efficiency evaluation in a two-stage network data envelopment analysis model with weight priority. Omega, 97. https://doi.org/10.1016/j.omega.2019.06.007.
[25] Yin, P., Chu, J., Wu, J., Ding, J., Yang, M. & Wang, Y. (2020). A DEA-based two-stage network approach for hotel performance analysis: An internal cooperation perspective. Omega, 93. https://doi.org/10.1016/j.omega.2019.02.004.
[26] Liu, H., Yang, G., Liu, X. & Song, Y. (2020). R&D performance assessment of industrial enterprises in China: A two-stage DEA approach. Socio-Economic Planning Sciences, 71. https://doi.org/10.1016/j.seps.2019.100753.
[27] Liu, X., Wu, X. & Zhang, W. (2024). A new DEA model and its application in performance evaluation of scientific research activities in the universities of Chinas double first-class initiative. Socio-Economic Planning Sciences, 92. https://doi.org/ 10.1016/j.seps. 2024.101839.
[28] Yu, A., Shi, Y., You, J. & Zhu, J. (2020). Innovation performance evaluation for high-tech companies using a dynamic network data envelopment analysis approach. European Journal of Operational Research, 292(5). https://doi.org/10.1016/j.ejor.2020.10.011.
[29] Wang, Y., Pan, J., Pei, R., Yi, B. & Yang, G. (2020). Assessing the technological innovation efficiency of China's high-tech industries with a two-stage network DEA approach. Socio-Economic Planning Sciences, 71. https://doi.org/10.1016/j.seps.2020.100810.
[30] Guan, J., Chen, K. (2012). Modeling the relative efficiency of national innovation systems. Research Policy, 41 (1), 102–115. https://doi.org/10.1016/j.respol.2011.07.001.
[31] Ma, J. (2015). A two-stage DEA model considering shared inputs and free intermediate measures. Expert Systems with Applications, 42, 4339-4347. https://doi.org/10.1016 /j.eswa.2015.01.040.
[32] Lee, J., Kim, C. & Choi, G. (2019). Exploring data envelopment analysis for measuring collaborated innovation efficiency of small and medium-sized enterprises in Korea. European Journal of Operational Research, 278 (2). https://doi.org/10.1016/j.ejor.2018.08.044.
[33] Chiu, S. H. & Lin, T. (2019). Evaluating of Regional Knowledge Innovation System in China: An Economic Framework Based on Dynamic Slacks-based Approach. Journal of Asian Finance, Economics and Business, 6(3), 141-149. DOI:10.13106/jafeb.2019.vol6.no3.141.
[34] Chun, D., Chung, Y., Woo, C., Seo, H. & Ko, H. (2015). Labor Union Effects on Innovation and Commercialization Productivity: An Integrated Propensity Score Matching and Two-Stage Data Envelopment Analysis. Sustainability, 7, 5120-5138. https://doi.org/10.3390/ su7055120.
[35] Xiong, X., Yang, G. L. & Guan, Z. (2018). Assessing R&D efficiency using a two-stage dynamics DEA model: A case study of research institutes in the Chines. Journal of Informetrics, 12, 784-805. DOI:10.1016/j.joi.2018.07.003.
[36] Wang, X., Liu, Y. & Chen, L. (2023). Innovation Efficiency Evaluation Based on a Two-Stage DEA Model With Shared-Input: A Case of Patent-Intensive Industry in China, in IEEE Transactions on Engineering Management, 70(5), 1808-1822. https://doi.org/10.1109/ TEM.2021.3068989.
[37] Zhang, B., Luo, Y. & Chiu, Y. H. (2019). Efficiency evaluation of Chinas high-tech industry with a multi-activity network data envelopment analysis approach. Socio-Economic Planning Sciences, 66, 2-9. https://doi.org/10.1016/j.seps.2018.07.013.
[38] Mills, E. F. E. A., Zeng, K., Fangbiao, L., & Fangyan, L. (2021). Modeling innovation efficiency, its micro-level drivers, and its impact on stock returns. Chaos, Solitons & Fractals, 152, 111303. https://doi.org/10.1016/j.chaos.2021.111303.
[39] Carayannis, E., Grigoroudis, E. & Goletsis, Y. (2016). A multilevel and multistage efficiency evaluation of innovation systems: A multiobjective DEA approach. Expert Systems With Applications, 62, 63-80. https://doi.org/10.1016/j.eswa.2016.06.017.
[40] Li, Y., Chen, Y., Liang, L. & Xie, J. (2012). DEA models for extended two-stage network structures. Omega, 40, 611-618. https://doi.org/10.1016/j.omega.2011.11.007.
[41] Liu, Z. & Lyu, J. (2020). Measuring the innovayion efficiency of the Chinese pharmaceutical industry based on a dynamic network DEA model. Applied Economics Letters, 27 (1), 35-40. https://doi.org/10.1080/13504851.2019.1606402.
[42] Fang, Z., Gui, W., Han, Z. & Lan, L. (2024). The efficiency evaluation and influencing factor analysis of regional green innovation: a refined dynamic network slacks-based measure approach. Kybernetes, 53, 6, 2153-2193. https://doi.org/10.1108/ K-03-2022-0420.
[43] Chen, X., Liu, Z. & Zhu, Q. (2018). Performance evaluation of Chinas high-tech innovation process: Analysis based on the innovation value chain. Technovation, 74, 42-53. https://d