Dynamic multi-attribute evaluation of digital economy development in China: a perspective from interaction effect
Abstract
This study aims to reflect the grey information coverage and complex interactions effect in digital economy development. Therefore, a multi-attribute decision making method based on the grey interaction relational degree of the normal cloud matrix (GIRD-NCM) model is proposed. First, the original information coverage grey numbers are transformed into normal cloud matrixes, and then a novel Minkowski distance between normal clouds is proposed by using different information principles. Second, the GIRD-NCM model is established according to the Choquet fuzzy integral and grey relational degree. Finally, the dynamic comprehensive evaluation of digital economy development in China from 2013 to 2020 is conducted. The implementation, availability, and feasibility of the GIRD-NCM model are verified by comparative analysis with three existing evaluation models. The empirical findings reveal a stable growth trend in China’s digital economy, with an annual growth rate of 7.87%, however, there are notable regional development disparities. The change in interaction degree has no effect on the rankings of provinces that are in the lead or have a moderately high level of digital economy development, but has a positive and negative impact on the rankings of these provinces with high and low levels of digital economy development, respectively.
Keyword : digital economy evaluation, grey relational degree, fuzzy integral, grey information coverage, normal cloud matrix
How to Cite
Xiao, Q., Gao, M., Chen, L., & Jiang, J. (2023). Dynamic multi-attribute evaluation of digital economy development in China: a perspective from interaction effect. Technological and Economic Development of Economy, 29(6), 1728–1752. https://doi.org/10.3846/tede.2023.20258
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Cao, Y., Yin, K., Li, X., & Zhai, C. (2021). Forecasting CO2 emissions from Chinese marine fleets using multivariable trend interaction grey model. Applied Soft Computing, 104, Article 107220. https://doi.org/10.1016/j.asoc.2021.107220
Chen, L., Dong, T., Peng, J., & Dan, R. (2022). Uncertainty analysis and optimization modelling with application to supply chain management: A systematic review. Mathematics, 11(11), Article 2530. https://doi.org/10.3390/math11112530
China Academy of Information and Communications Technology. (2021). White paper on China’s Digital Economy. Retrieved July 20, 2022 from https://www.100ec.cn/detail--6591053.html
Chu, J., & Xiao, X. (2023). Benefits evaluation of the Northeast Passage based on grey relational degree of discrete Z-numbers. Information Sciences, 626, 607–625. https://doi.org/10.1016/j.ins.2023.02.085
Cui, L., Chan, H. K., Zhou, Y., Dai, J., & Lim, J. J. (2019). Exploring critical factors of green business failure based on Grey-Decision Making Trial and Evaluation Laboratory (DEMATEL). Journal of Business Research, 98, 450–461. https://doi.org/10.1016/j.jbusres.2018.03.031
Dai, G. Z., Pan, Q., Zhang, S. Y., & Zhang, H. C. (1999). The developments and problems in evidence reasoning. Control Theory and Applications, 16(4), 465–469.
Ding, S., Dang, Y. G., Ning, X., & Wang, J. J. (2018). Multivariable grey forecasting model based on interaction effect and its application. Journal of Systems Engineering and Electronics, 40(3), 595–602.
Goldstein, M. (2006). Subjective Bayesian analysis: Principles and practice. Bayesian Analysis, 1(3), 403–420. https://doi.org/10.1214/06-BA116
Gong, Y. B., Xu, X. K., & Liu, G. F. (2021). Research on linguistic multi-attribute decision marking method based on normal cloud expectation and variance distance. Statistics & Information Forum, 36(10), 12–19.
Hong, Y. Z., & Chang, H. H. (2020). Does digitalization affect the objective and subjective wellbeing of forestry farm households? Empirical evidence in Fujian Province of China. Forest Policy and Economics, 118, Article 102236. https://doi.org/10.1016/j.forpol.2020.102236
He, J., Mao, S., & Kang, Y. (2023). Augmented fractional accumulation grey model and its application: Class ratio and restore error perspectives. Mathematics and Computers in Simulation, 209, 220–247. https://doi.org/10.1016/j.matcom.2023.02.008
Horoshko, O. I., Horoshko, A., Bilyuga, S., & Horoshko, V. (2021). Theoretical and methodological bases of the study of the impact of digital economy on world policy in 21 century. Technological Forecasting and Social Change, 166, Article 120640. https://doi.org/10.1016/j.techfore.2021.120640
Huang, C. Y., Hsu, C. C., Chiou, M. L., & Chen, C. I. (2020). The main factors affecting Taiwan’s economic growth rate via dynamic grey relational analysis. PLoS ONE, 15(10), Article e0240065. https://doi.org/10.1371/journal.pone.0240065
Jana, C., & Pal, M. (2021). A dynamical hybrid method to design decision making process based on GRA approach for multiple attributes problem. Engineering Applications of Artificial Intelligence, 100, Article 104203. https://doi.org/10.1016/j.engappai.2021.104203
Jiang, H., & Murmann, J. P. (2022). The rise of China’s digital economy: An overview. Management and Organization Review, 18(4), 790–802. https://doi.org/10.1017/mor.2022.32
Kosimov, J., & Ruziboyeva, G. (2022). The role of the digital economy in the world. Scientific Progress, 3(2), 435–441.
Li, D., Liu, C., & Gan, W. (2009). A new cognitive model: Cloud model. International Journal of Intelligent Systems, 24(3), 357–375. https://doi.org/10.1002/int.20340
Li, Y., Rao, C., Goh, M., & Xiao, X. (2022). Novel multi-attribute decision-making method based on Z-number grey relational degree. Soft Computing, 26, 13333–13347. https://doi.org/10.1007/s00500-022-07487-w
Lian, X., Mu, Y., & Zhang, W. (2023). Digital inclusive financial services and rural income: Evidence from China’s major grain-producing regions. Finance Research Letters, 53, Article 103622. https://doi.org/10.1016/j.frl.2022.103622
Liu, H. C., Wang, L. E., Li, Z., & Hu, Y. P. (2018). Improving risk evaluation in FMEA with cloud model and hierarchical TOPSIS method. IEEE Transactions on Fuzzy Systems, 27(1), 84–95. https://doi.org/10.1109/TFUZZ.2018.2861719
Meng, M., & Qu, D. (2022). Understanding the green energy efficiencies of provinces in China: A Super-SBM and GML analysis. Energy, 239, Article 121912. https://doi.org/10.1016/j.energy.2021.121912
Matthess, M., & Kunkel, S. (2020). Structural change and digitalization in developing countries: Conceptually linking the two transformations. Technology in Society, 63, Article 101428. https://doi.org/10.1016/j.techsoc.2020.101428
Muñoz, J., Molero-Castillo, G., Benítez-Guerrero, E., & Barcenas, E. (2018). Data fusion as source for the generation of useful knowledge in context-aware systems. Journal of Intelligent & Fuzzy Systems, 34(5), 3165–3176. https://doi.org/10.3233/JIFS-169500
Pan, W., Xie, T., Wang, Z., & Ma, L. (2022). Digital economy: An innovation driver for total factor productivity. Journal of Business Research, 139, 303–311. https://doi.org/10.1016/j.jbusres.2021.09.061
Rao, C., Gao, M., Wen, J., & Goh, M. (2022). Multi-attribute group decision making method with dual comprehensive clouds under information environment of dual uncertain Z-numbers. Information Sciences, 602, 106–127. https://doi.org/10.1016/j.ins.2022.04.031
Sugeno, M. (1974). Theory of fuzzy integral and its applications. Tokyo Institute of Technology.
Wang, J., Dong, K., Dong, X., & Taghizadeh-Hesary, F. (2022). Assessing the digital economy and its carbon-mitigation effects: The case of China. Energy Economics, 113, Article 106198. https://doi.org/10.1016/j.eneco.2022.106198
Wang, J. Q., & Liu, T. (2012). Uncertain linguistic multi-criteria group decision-making approach based on integrated cloud. Control and Decision, 27(8), 1185–1190.
Wang, X. S., Wen, X, Q., Liu, D., & Wang, W. J. (2010). Trust model based on cloud theory in pervasive environment. Computer Engineering, 36(7), 282–286.
Xiao, L., Huang, G., & Zhang, G. (2021a). Improved assessment model for candidate design schemes with an interval rough integrated cloud model under uncertain group environment. Engineering Applications of Artificial Intelligence, 104, Article 104352. https://doi.org/10.1016/j.engappai.2021.104352
Xiao, Q., Shan, M., Gao, M., Xiao, X., & Guo, H. (2021b). Evaluation of the coordination between China’s technology and economy using a grey multivariate coupling model. Technological and Economic Development of Economy, 27(1), 24–44. https://doi.org/10.3846/tede.2020.13742
Xiao, Q., Shan, M., Gao, M., & Xiao, X. (2020a). Grey information coverage interaction relational decision making and its application. Journal of Systems Engineering and Electronics, 31(2), 359–369. https://doi.org/10.23919/JSEE.2020.000013
Xiao, Q., Shan, M., Xiao, X., & Rao, C. (2020b). Evaluation model of industrial operation quality under multi-source heterogeneous data information. International Journal of Fuzzy Systems, 22(2), 522–547. https://doi.org/10.1007/s40815-019-00776-x
Xiao, X. P., & Mao, S. H. (2013). Grey prediction and decision making method. Science Press.
Yang, S., & He, J. (2022). Analysis of digital economy development based on AHP-entropy weight method. Journal of Sensors, 2022, Article 7642682. https://doi.org/10.1155/2022/7642682
Yue, Z. L. (2011). An extended TOPSIS for determining weights of decision makers with interval numbers. Knowledge-Based Systems, 24(1), 146–153. https://doi.org/10.1016/j.knosys.2010.07.014
Zhang, J., Lyu, Y., Li, Y., & Geng, Y. (2022a). Digital economy: An innovation driving factor for low-carbon development. Environmental Impact Assessment Review, 96, Article 106821. https://doi.org/10.1016/j.eiar.2022.106821
Zhang, T., Yan, L., & Yang, Y. (2018). Trust evaluation method for clustered wireless sensor networks based on cloud model. Wireless Networks, 24(3), 777–797. https://doi.org/10.1007/s11276-016-1368-y
Zhang, S., Xiang, M., Xu, Z., Wang, L., & Zhang, C. (2020). Evaluation of water cycle health status based on a cloud model. Journal of Cleaner Production, 245, Article 118850. https://doi.org/10.1016/j.jclepro.2019.118850
Zhang, X., Rao, C., Xiao, X., Hu, F., & Goh, M. (2024). Prediction of demand for staple food and feed grain by a novel hybrid fractional discrete multivariate grey model. 125, 85-107. https://doi.org/10.1016/j.apm.2023.09.026
Zhang, X, Y., Li, S. S., & Hu, Y. (2022b). Analysis on the ecosystem service protection effect of national nature reserve in Qinghai-Tibetan Plateau from weight perspective. Ecological Indicators, 142, Article 109225. https://doi.org/10.1016/j.ecolind.2022.109225
Zhang, Y., Jiang, C., Yue, B., Wan, J., & Guizani, M. (2022c). Information fusion for edge intelligence: A survey. Information Fusion, 81, 171–186. https://doi.org/10.1016/j.inffus.2021.11.018
Zhao, H., Mi, J., & Liang, M. (2022). A multi-granularity information fusion method based on logistic regression model and Dempster-Shafer evidence theory and its application. International Journal of Machine Learning and Cybernetics, 13, 3131–3142. https://doi.org/10.1007/s13042-022-01584-w
Zhu, W., Duan, L., Zhang, J., Shi, Y., & Qiao, Y. (2015). Constructing a competitiveness evaluation system of listed Chinese medicine enterprises based on grey correlation. The Journal of Grey System, 27(4), 40–52.
Zhu, W., Huang, J., & Cai, N. (2022). Comparing the digital economy urban network: Study based on the human resource needs in the Yangtze River Delta, China. Journal of Urban Planning and Development, 148(4), Article 05022033. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000886
Chen, L., Dong, T., Peng, J., & Dan, R. (2022). Uncertainty analysis and optimization modelling with application to supply chain management: A systematic review. Mathematics, 11(11), Article 2530. https://doi.org/10.3390/math11112530
China Academy of Information and Communications Technology. (2021). White paper on China’s Digital Economy. Retrieved July 20, 2022 from https://www.100ec.cn/detail--6591053.html
Chu, J., & Xiao, X. (2023). Benefits evaluation of the Northeast Passage based on grey relational degree of discrete Z-numbers. Information Sciences, 626, 607–625. https://doi.org/10.1016/j.ins.2023.02.085
Cui, L., Chan, H. K., Zhou, Y., Dai, J., & Lim, J. J. (2019). Exploring critical factors of green business failure based on Grey-Decision Making Trial and Evaluation Laboratory (DEMATEL). Journal of Business Research, 98, 450–461. https://doi.org/10.1016/j.jbusres.2018.03.031
Dai, G. Z., Pan, Q., Zhang, S. Y., & Zhang, H. C. (1999). The developments and problems in evidence reasoning. Control Theory and Applications, 16(4), 465–469.
Ding, S., Dang, Y. G., Ning, X., & Wang, J. J. (2018). Multivariable grey forecasting model based on interaction effect and its application. Journal of Systems Engineering and Electronics, 40(3), 595–602.
Goldstein, M. (2006). Subjective Bayesian analysis: Principles and practice. Bayesian Analysis, 1(3), 403–420. https://doi.org/10.1214/06-BA116
Gong, Y. B., Xu, X. K., & Liu, G. F. (2021). Research on linguistic multi-attribute decision marking method based on normal cloud expectation and variance distance. Statistics & Information Forum, 36(10), 12–19.
Hong, Y. Z., & Chang, H. H. (2020). Does digitalization affect the objective and subjective wellbeing of forestry farm households? Empirical evidence in Fujian Province of China. Forest Policy and Economics, 118, Article 102236. https://doi.org/10.1016/j.forpol.2020.102236
He, J., Mao, S., & Kang, Y. (2023). Augmented fractional accumulation grey model and its application: Class ratio and restore error perspectives. Mathematics and Computers in Simulation, 209, 220–247. https://doi.org/10.1016/j.matcom.2023.02.008
Horoshko, O. I., Horoshko, A., Bilyuga, S., & Horoshko, V. (2021). Theoretical and methodological bases of the study of the impact of digital economy on world policy in 21 century. Technological Forecasting and Social Change, 166, Article 120640. https://doi.org/10.1016/j.techfore.2021.120640
Huang, C. Y., Hsu, C. C., Chiou, M. L., & Chen, C. I. (2020). The main factors affecting Taiwan’s economic growth rate via dynamic grey relational analysis. PLoS ONE, 15(10), Article e0240065. https://doi.org/10.1371/journal.pone.0240065
Jana, C., & Pal, M. (2021). A dynamical hybrid method to design decision making process based on GRA approach for multiple attributes problem. Engineering Applications of Artificial Intelligence, 100, Article 104203. https://doi.org/10.1016/j.engappai.2021.104203
Jiang, H., & Murmann, J. P. (2022). The rise of China’s digital economy: An overview. Management and Organization Review, 18(4), 790–802. https://doi.org/10.1017/mor.2022.32
Kosimov, J., & Ruziboyeva, G. (2022). The role of the digital economy in the world. Scientific Progress, 3(2), 435–441.
Li, D., Liu, C., & Gan, W. (2009). A new cognitive model: Cloud model. International Journal of Intelligent Systems, 24(3), 357–375. https://doi.org/10.1002/int.20340
Li, Y., Rao, C., Goh, M., & Xiao, X. (2022). Novel multi-attribute decision-making method based on Z-number grey relational degree. Soft Computing, 26, 13333–13347. https://doi.org/10.1007/s00500-022-07487-w
Lian, X., Mu, Y., & Zhang, W. (2023). Digital inclusive financial services and rural income: Evidence from China’s major grain-producing regions. Finance Research Letters, 53, Article 103622. https://doi.org/10.1016/j.frl.2022.103622
Liu, H. C., Wang, L. E., Li, Z., & Hu, Y. P. (2018). Improving risk evaluation in FMEA with cloud model and hierarchical TOPSIS method. IEEE Transactions on Fuzzy Systems, 27(1), 84–95. https://doi.org/10.1109/TFUZZ.2018.2861719
Meng, M., & Qu, D. (2022). Understanding the green energy efficiencies of provinces in China: A Super-SBM and GML analysis. Energy, 239, Article 121912. https://doi.org/10.1016/j.energy.2021.121912
Matthess, M., & Kunkel, S. (2020). Structural change and digitalization in developing countries: Conceptually linking the two transformations. Technology in Society, 63, Article 101428. https://doi.org/10.1016/j.techsoc.2020.101428
Muñoz, J., Molero-Castillo, G., Benítez-Guerrero, E., & Barcenas, E. (2018). Data fusion as source for the generation of useful knowledge in context-aware systems. Journal of Intelligent & Fuzzy Systems, 34(5), 3165–3176. https://doi.org/10.3233/JIFS-169500
Pan, W., Xie, T., Wang, Z., & Ma, L. (2022). Digital economy: An innovation driver for total factor productivity. Journal of Business Research, 139, 303–311. https://doi.org/10.1016/j.jbusres.2021.09.061
Rao, C., Gao, M., Wen, J., & Goh, M. (2022). Multi-attribute group decision making method with dual comprehensive clouds under information environment of dual uncertain Z-numbers. Information Sciences, 602, 106–127. https://doi.org/10.1016/j.ins.2022.04.031
Sugeno, M. (1974). Theory of fuzzy integral and its applications. Tokyo Institute of Technology.
Wang, J., Dong, K., Dong, X., & Taghizadeh-Hesary, F. (2022). Assessing the digital economy and its carbon-mitigation effects: The case of China. Energy Economics, 113, Article 106198. https://doi.org/10.1016/j.eneco.2022.106198
Wang, J. Q., & Liu, T. (2012). Uncertain linguistic multi-criteria group decision-making approach based on integrated cloud. Control and Decision, 27(8), 1185–1190.
Wang, X. S., Wen, X, Q., Liu, D., & Wang, W. J. (2010). Trust model based on cloud theory in pervasive environment. Computer Engineering, 36(7), 282–286.
Xiao, L., Huang, G., & Zhang, G. (2021a). Improved assessment model for candidate design schemes with an interval rough integrated cloud model under uncertain group environment. Engineering Applications of Artificial Intelligence, 104, Article 104352. https://doi.org/10.1016/j.engappai.2021.104352
Xiao, Q., Shan, M., Gao, M., Xiao, X., & Guo, H. (2021b). Evaluation of the coordination between China’s technology and economy using a grey multivariate coupling model. Technological and Economic Development of Economy, 27(1), 24–44. https://doi.org/10.3846/tede.2020.13742
Xiao, Q., Shan, M., Gao, M., & Xiao, X. (2020a). Grey information coverage interaction relational decision making and its application. Journal of Systems Engineering and Electronics, 31(2), 359–369. https://doi.org/10.23919/JSEE.2020.000013
Xiao, Q., Shan, M., Xiao, X., & Rao, C. (2020b). Evaluation model of industrial operation quality under multi-source heterogeneous data information. International Journal of Fuzzy Systems, 22(2), 522–547. https://doi.org/10.1007/s40815-019-00776-x
Xiao, X. P., & Mao, S. H. (2013). Grey prediction and decision making method. Science Press.
Yang, S., & He, J. (2022). Analysis of digital economy development based on AHP-entropy weight method. Journal of Sensors, 2022, Article 7642682. https://doi.org/10.1155/2022/7642682
Yue, Z. L. (2011). An extended TOPSIS for determining weights of decision makers with interval numbers. Knowledge-Based Systems, 24(1), 146–153. https://doi.org/10.1016/j.knosys.2010.07.014
Zhang, J., Lyu, Y., Li, Y., & Geng, Y. (2022a). Digital economy: An innovation driving factor for low-carbon development. Environmental Impact Assessment Review, 96, Article 106821. https://doi.org/10.1016/j.eiar.2022.106821
Zhang, T., Yan, L., & Yang, Y. (2018). Trust evaluation method for clustered wireless sensor networks based on cloud model. Wireless Networks, 24(3), 777–797. https://doi.org/10.1007/s11276-016-1368-y
Zhang, S., Xiang, M., Xu, Z., Wang, L., & Zhang, C. (2020). Evaluation of water cycle health status based on a cloud model. Journal of Cleaner Production, 245, Article 118850. https://doi.org/10.1016/j.jclepro.2019.118850
Zhang, X., Rao, C., Xiao, X., Hu, F., & Goh, M. (2024). Prediction of demand for staple food and feed grain by a novel hybrid fractional discrete multivariate grey model. 125, 85-107. https://doi.org/10.1016/j.apm.2023.09.026
Zhang, X, Y., Li, S. S., & Hu, Y. (2022b). Analysis on the ecosystem service protection effect of national nature reserve in Qinghai-Tibetan Plateau from weight perspective. Ecological Indicators, 142, Article 109225. https://doi.org/10.1016/j.ecolind.2022.109225
Zhang, Y., Jiang, C., Yue, B., Wan, J., & Guizani, M. (2022c). Information fusion for edge intelligence: A survey. Information Fusion, 81, 171–186. https://doi.org/10.1016/j.inffus.2021.11.018
Zhao, H., Mi, J., & Liang, M. (2022). A multi-granularity information fusion method based on logistic regression model and Dempster-Shafer evidence theory and its application. International Journal of Machine Learning and Cybernetics, 13, 3131–3142. https://doi.org/10.1007/s13042-022-01584-w
Zhu, W., Duan, L., Zhang, J., Shi, Y., & Qiao, Y. (2015). Constructing a competitiveness evaluation system of listed Chinese medicine enterprises based on grey correlation. The Journal of Grey System, 27(4), 40–52.
Zhu, W., Huang, J., & Cai, N. (2022). Comparing the digital economy urban network: Study based on the human resource needs in the Yangtze River Delta, China. Journal of Urban Planning and Development, 148(4), Article 05022033. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000886