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Evaluating technology innovation capabilities of companies based on entropy- TOPSIS: the case of solar cell companies

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Abstract

Technological innovation capability (TIC) refers to a fundamental ability owned by an organization to invest and reorganize production factors in technology innovation for gaining competitive advantages. It has attracted increasing attention to evaluate enterprises’ TIC in a competitive market. However, the existing methods have some limitations in the accuracy and efficiency of assessing TIC. This paper proposes a novel approach of evaluating enterprises’ TIC, which combines the entropy weight method and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) based on patent information. Entropy can determine the weights of indicators that help improve the rationality of the weighting. Besides, TOPSIS may reasonably rank the calculating results, which helps solve poor results in a fuzzy comprehensive evaluation. The paper takes seven enterprises in the solar cell technology field as samples to illustrate the proposed method. The results show that the Entropy-TOPSIS method can effectively evaluate enterprises’ TIC and is more suitable for small samples.

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References

  1. Wang C-H, Lu I-Y, Chen C-B (2008) Evaluating firm technological innovation capability under uncertainty. Technovation 28(6):349–363

    Article  Google Scholar 

  2. Yam RC, Guan JC, Pun KF, Tang EP (2004) An audit of technological innovation capabilities in Chinese firms: some empirical findings in Beijing. China Res policy 33(8):1123–1140

    Article  Google Scholar 

  3. L.E. Westphal, Y.W. Rhee, G. Pursell, B. Mundial, Korean industrial competence: where it came from, World Bank Washington, DC1981.

  4. Milliken J, Joseck F, Wang M, Yuzugullu E (2007) The advanced energy initiative. J Power Sources 172(1):121–131

    Article  Google Scholar 

  5. Antoniou PH, Ansoff HI (2004) Strategic management of technology. Technol Anal Strat Manag 16(2):275–291

    Article  Google Scholar 

  6. Duchêne A, Sen D, Serfes K (2015) Patent licensing and entry deterrence: The role of low royalties. Economica 82:1324–1348

    Article  Google Scholar 

  7. Cockburn IM, MacGarvie MJ (2011) Entry and patenting in the software industry. Manage Sci 57(5):915–933

    Article  Google Scholar 

  8. Devlin G, Klvač R (2014) How Technology Can Improve the Efficiency of Excavator-Based Cable Harvesting for Potential Biomass Extraction—A Woody Productivity Resource and Cost Analysis for Ireland. Energies 7(12):8374–8395

    Article  Google Scholar 

  9. Jun S-P, Yeom J, Son J-K (2014) A study of the method using search traffic to analyze new technology adoption. Technol Forecast Soc Chang 81:82–95

    Article  Google Scholar 

  10. Gupta AK, Gupta N (2019) Innovation and culture as a dynamic capability for firm performance: A study from emerging markets. Glob J Flex Syst Manag 20(4):323–336

    Article  Google Scholar 

  11. Lyu H-M, Zhou W-H, Shen S-L, Zhou A-N (2020) Inundation risk assessment of metro system using AHP and TFN-AHP in Shenzhen. Sustain Cities Soc 56:102103

    Article  Google Scholar 

  12. Zeng Z (2016) A novel model for enterprise technological innovation capability evaluation with 2-tuple linguistic information. J Intelligent Fuzzy Syst 31(1):541–546

    Article  Google Scholar 

  13. Wu S-Q (2017) Models for evaluating the technological innovation capability of small and micro enterprises with hesitant fuzzy information. J Intelligent Fuzzy Syst 32(1):249–256

    Article  Google Scholar 

  14. Sueyoshi T, Goto M (2014) Environmental assessment for corporate sustainability by resource utilization and technology innovation: DEA radial measurement on Japanese industrial sectors. Energy Econ 46:295–307

    Article  Google Scholar 

  15. Guan JC, Yam RC, Mok CK, Ma N (2006) A study of the relationship between competitiveness and technological innovation capability based on DEA models. Eur J Oper Res 170(3):971–986

    Article  Google Scholar 

  16. Pei NS (2008) Enhancing knowledge creation in organizations. Commun IBIMA 3(2):1–6

    Google Scholar 

  17. Hailekiros GS, Renyong H (2016) The effect of organizational learning capability on firm performance: Mediated by technological innovation capability. Eur J Business Manag 8(30):87–95

    Google Scholar 

  18. Cheng C, Yang M (2017) Enhancing performance of cross-border mergers and acquisitions in developed markets: The role of business ties and technological innovation capability. J Bus Res 81:107–117

    Article  Google Scholar 

  19. Su Y, Yu Y-Q (2019) Spatial interaction network structure and its influence on new energy enterprise technological innovation capability: Evidence from China. J Renewable Sustain Energy 11(2):025902

    Article  Google Scholar 

  20. Wang W, Zhang C (2018) Evaluation of relative technological innovation capability: Model and case study for China’s coal mine. Resour Policy 58:144–149

    Article  Google Scholar 

  21. Camisón C, Villar-López A (2014) Organizational innovation as an enabler of technological innovation capabilities and firm performance. J Bus Res 67(1):2891–2902

    Article  Google Scholar 

  22. Çakar ND, Ertürk A (2010) Comparing innovation capability of small and medium-sized enterprises: examining the effects of organizational culture and empowerment. J Small Bus Manage 48(3):325–359

    Article  Google Scholar 

  23. Guan J, Ma N (2003) Innovative capability and export performance of Chinese firms. Technovation 23(9):737–747

    Article  Google Scholar 

  24. Y. bin Zainuddin, (2017) Moderating effect of environmental turbulence on firm’s technological innovation capabilities (TIC) and business performance in the automotive industry in Malaysia: A conceptual framework. MATEC web of Conferences, EDP Sciences 90:01009

    Article  Google Scholar 

  25. Hansen UE, Ockwell D (2014) Learning and technological capability building in emerging economies: The case of the biomass power equipment industry in Malaysia. Technovation 34(10):617–630

    Article  Google Scholar 

  26. Hong Y, Niu D, Xiao B, Wu L (2015) Comprehensive evaluation of the technology innovation capability of China’s high-tech industries based on fuzzy borda combination method. Int J Innovation Sci 7:215–230

    Article  Google Scholar 

  27. Lai K-K, Lin C-Y, Chang Y-H, Yang M-C, Yang W-G (2017) A structured approach to explore technological competencies through R&D portfolio of photovoltaic companies by patent statistics. Scientometrics 111(3):1327–1351

    Article  Google Scholar 

  28. Leydesdorff L, Alkemade F, Heimeriks G, Hoekstra R (2015) Patents as instruments for exploring innovation dynamics: geographic and technological perspectives on “photovoltaic cells.” Scientometrics 102(1):629–651

    Article  Google Scholar 

  29. Qiu H-H, Yang J (2018) An assessment of technological innovation capabilities of carbon capture and storage technology based on patent analysis: A comparative study between china and the United States. Sustainability 10(3):877

    Article  Google Scholar 

  30. Zhang L, Wang J, Wen H, Fu Z, Li X (2016) Operating performance, industry agglomeration and its spatial characteristics of Chinese photovoltaic industry. Renew Sustain Energy Rev 65:373–386

    Article  Google Scholar 

  31. Türker MV (2012) A model proposal oriented to measure technological innovation capabilities of business firms–a research on automotive industry. Procedia Soc Behav Sci 41:147–159

    Article  Google Scholar 

  32. Tseng C-Y (2014) Technological innovation capability, knowledge sourcing and collaborative innovation in Gulf Cooperation Council countries. Innovation 16(2):212–223

    Article  Google Scholar 

  33. Tseng C-Y (2009) Technological innovation and knowledge network in Asia: Evidence from comparison of information and communication technologies among six countries. Technol Forecast Soc Chang 76(5):654–663

    Article  Google Scholar 

  34. Chen Y, Yang Z, Shu F, Hu Z, Meyer M, Bhattacharya S (2009) A patent based evaluation of technological innovation capability in eight economic regions in PR China. World Patent Inf 31(2):104–110

    Article  Google Scholar 

  35. Huang C-C, Chu P-Y, Chiang Y-H (2008) A fuzzy AHP application in government-sponsored R&D project selection. Omega 36(6):1038–1052

    Article  Google Scholar 

  36. Ferreira PJS, Dionísio ATM (2016) What are the conditions for good innovation results? A fuzzy-set approach for European Union, J Bus Res 69(11):5396–5400

    Google Scholar 

  37. Wang B, Gu X, Ma L, Yan S (2017) Temperature error correction based on BP neural network in meteorological wireless sensor network. Int J Sens Net 23(4):265–278

    Article  Google Scholar 

  38. J. Maleyeff, Quantitative models for performance evaluation and benchmarking: DEA with spreadsheets and DEA excel solver, Benchmarking: An International Journal (2005).

  39. Hwang C-L, Yoon K (1981) Methods for multiple attribute decision making. Springer, Multiple attribute decision making, pp 58–191

    Google Scholar 

  40. Huang W, Shuai B, Sun Y, Wang Y, Antwi E (2018) Using entropy-TOPSIS method to evaluate urban rail transit system operation performance: The China case. Transportation Res Part A: Policy and Practice 111:292–303

    Google Scholar 

  41. Behzadian M, Otaghsara SK, Yazdani M, Ignatius J (2012) A state-of the-art survey of TOPSIS applications. Expert Syst Appl 39(17):13051–13069

    Article  Google Scholar 

  42. Kuo T (2017) A modified TOPSIS with a different ranking index. Eur J Oper Res 260(1):152–160

    Article  Google Scholar 

  43. Walczak D, Rutkowska A (2017) Project rankings for participatory budget based on the fuzzy TOPSIS method. Eur J Oper Res 260(2):706–714

    Article  Google Scholar 

  44. Shannon CE (2001) A mathematical theory of communication. ACM SIGMOBILE mobile computing and communications review 5(1):3–55

    Article  Google Scholar 

  45. Maghsoodi AI, Abouhamzeh G, Khalilzadeh M, Zavadskas EK (2018) Ranking and selecting the best performance appraisal method using the MULTIMOORA approach integrated Shannon’s entropy. Front Bus Res China 12(1):1–21

    Google Scholar 

  46. Yuan X, Cai Y (2021) Forecasting the development trend of low emission vehicle technologies: Based on patent data. Technol Forecasting Soc Change 166:120651

    Article  Google Scholar 

  47. Cao C, Slobounov S (2011) Application of a novel measure of EEG non-stationarity as ‘Shannon-entropy of the peak frequency shifting’for detecting residual abnormalities in concussed individuals. Clin Neurophysiol 122(7):1314–1321

    Article  Google Scholar 

  48. Srivastav RK, Simonovic SP (2014) An analytical procedure for multi-site, multi-season streamflow generation using maximum entropy bootstrapping. Environ Model Softw 59:59–75

    Article  Google Scholar 

  49. Delgado A, Romero I (2016) Environmental conflict analysis using an integrated grey clustering and entropy-weight method: A case study of a mining project in Peru. Environ Model Softw 77:108–121

    Article  Google Scholar 

  50. Li P, Wu J, Qian H (2013) Assessment of groundwater quality for irrigation purposes and identification of hydrogeochemical evolution mechanisms in Pengyang County. China, Environ Earth Sci 69(7):2211–2225

    Article  Google Scholar 

  51. Kroll H (2011) Exploring the validity of patent applications as an indicator of Chinese competitiveness and market structure. World Patent Inf 33(1):23–33

    Article  Google Scholar 

  52. Lee C, Kwon O, Kim M, Kwon D (2018) Early identification of emerging technologies: A machine learning approach using multiple patent indicators. Technol Forecast Soc Chang 127:291–303

    Article  Google Scholar 

  53. Fischer T, Leidinger J (2014) Testing patent value indicators on directly observed patent value—An empirical analysis of Ocean Tomo patent auctions. Res Policy 43(3):519–529

    Article  Google Scholar 

  54. Yuan X, Li X (2020) A network analytic method for measuring patent thickets: A case of FCEV technology. Technol Forecasting Soc Change 156:120038

    Article  Google Scholar 

  55. Sterzi V (2013) Patent quality and ownership: An analysis of UK faculty patenting. Res Policy 42(2):564–576

    Article  Google Scholar 

  56. J. Baron, H. Delcamp, Patent quality and value in discrete and cumulative innovation, CERNA Working Paper (2010–07) (2010).

  57. Dang J, Motohashi K (2015) Patent statistics: A good indicator for innovation in China? Patent subsidy program impacts on patent quality, China Economic Review 35:137–155

    Google Scholar 

  58. Yuan X, Li X (2021) Mapping the technology diffusion of battery electric vehicle based on patent analysis: A perspective of global innovation systems. Energy 222:119897

    Article  Google Scholar 

  59. Park I, Lee K, Yoon B (2015) Exploring promising research frontiers based on knowledge maps in the solar cell technology field. Sustainability 7(10):13660–13689

    Article  Google Scholar 

  60. Ernst H (2001) Patent applications and subsequent changes of performance: evidence from time-series cross-section analyses on the firm level. Res Policy 30(1):143–157

    Article  Google Scholar 

  61. Ernst H, Omland N (2011) The Patent Asset Index–A new approach to benchmark patent portfolios. World Patent Inf 33(1):34–41

    Article  Google Scholar 

  62. He Z, Zhong C, Su S, Xu M, Wu H, Cao Y (2012) Enhanced power-conversion efficiency in polymer solar cells using an inverted device structure. Nat Photonics 6(9):591–595

    Article  Google Scholar 

  63. Acs ZJ, Anselin L, Varga A (2002) Patents and innovation counts as measures of regional production of new knowledge. Res Policy 31(7):1069–1085

    Article  Google Scholar 

  64. Fai FM (2005) Using intellectual property data to analyse China’s growing technological capabilities. World Patent Inf 27(1):49–61

    Article  Google Scholar 

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  1. School of Management, Huazhong University of Science and Technology, Wuhan, China

    Xiaodong Yuan & Weiling Song

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Yuan, X., Song, W. Evaluating technology innovation capabilities of companies based on entropy- TOPSIS: the case of solar cell companies. Inf Technol Manag 23, 65–76 (2022). https://doi.org/10.1007/s10799-021-00344-6

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