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Evaluation of city innovation capability using the TOPSIS-based order relation method: The case of Liaoning province, China

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  • Chen, Yang
  • Li, Weiwei
  • Yi, Pingtao

Abstract

Innovation in the knowledge economy is now widely acknowledged as the most important factor accelerating the economic growth at the level of country, region, and city. This paper proposes the TOPSIS-based Order Relation Method (TOPSIS-ORM) to measure and rank the city innovation capability in Liaoning province from 2012 to 2016. For this purpose, an indicator system that includes a set of 12 indicators is first established from the dimensions of innovation input, output, and environment. To determine the indicator weights, the increment and growth rate of each indicator are simultaneously considered in the calculation of the importance of the associated indicator by using dynamic-TOPSIS, and then the ORM is chosen to compute the weighting system. The results show that the innovation capability of most cities in Liaoning province was extremely poor, showing a dramatically downward trend during the studied years. However, Shenyang and Dalian, which have high political hierarchy, achieved a comparatively high innovation performance. Furthermore, cities in southern Liaoning province achieved the highest evaluation values, and the innovation performance of western cities were lagging, which indicates a “hot spot in the South” and “cold spot in the West” characteristic. To illustrate different dimensions, innovation input, output, and environment showed significant fluctuation. By comparing with different indicators, we find the ratio of internet users (C10) to be the primary driving indicator for promoting innovation capability, whereas the ratio of college students (C12) is the common obstacle to improving innovation performance.

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  • Chen, Yang & Li, Weiwei & Yi, Pingtao, 2020. "Evaluation of city innovation capability using the TOPSIS-based order relation method: The case of Liaoning province, China," Technology in Society, Elsevier, vol. 63(C).
    • Handle: RePEc:eee:teinso:v:63:y:2020:i:c:s0160791x19303859
    DOI: 10.1016/j.techsoc.2020.101330
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    3. Xu Linming & Lu Jincheng & Li Meijuan & He Lerong, 2021. "Dynamic Evaluation and Analysis of Regional Innovation Capability in Eastern China from the Perspective of High-quality Development," Journal of Systems Science and Information, De Gruyter, vol. 9(6), pages 608-626, December.
    4. Saraji, Mahyar Kamali & Streimikiene, Dalia, 2022. "Evaluating the circular supply chain adoption in manufacturing sectors: A picture fuzzy approach," Technology in Society, Elsevier, vol. 70(C).
    5. Gao, Kang & Yuan, Yijun, 2022. "Government intervention, spillover effect and urban innovation performance: Empirical evidence from national innovative city pilot policy in China," Technology in Society, Elsevier, vol. 70(C).
    6. Marti, Luisa & Puertas, Rosa, 2023. "Analysis of European competitiveness based on its innovative capacity and digitalization level," Technology in Society, Elsevier, vol. 72(C).
    7. Pu, Xiaohong & Zeng, Ming & Zhang, Weike, 2023. "Corporate sustainable development driven by high-quality innovation: Does fiscal decentralization really matter?," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 273-289.
    8. Zaohong Liu & Zhangzejun Jiang & Chen Xu & Guanjun Cai & Jian Zhan, 2021. "Assessment of provincial waterlogging risk based on entropy weight TOPSIS–PCA method," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(2), pages 1545-1567, September.

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