A study of the relationship between competitiveness and technological innovation capability based on DEA models

Guan, Jian; Yam, Richard C.M.; Mok, C. K.; Ma, Ning

doi:10.1016/j.ejor.2004.07.054

Cited by 299 publications

(217 citation statements)

References 39 publications

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“…Guan et al (2006) assert that an exploration of quantitative relationships between technological innovation capability and competitiveness suggests a close internal relationship between these two variables. Similarly, Castellacci (2008) The competitiveness of an industry is analysed generally on the basis of a single perspective.…”

Section: Industry Competitivenessmentioning

confidence: 99%

A decision support methodology to enhance the competitiveness of the Turkish automotive industry

Ülengin

Önsel

Aktaş

et al. 2014

European Journal of Operational Research

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Three levels of competitiveness affect the success of business enterprises in a globally competitive environment: the competitiveness of the company, the competitiveness of the industry in which the company operates and the competitiveness of the country where the business is located. This study analyses the competitiveness of the automotive industry in association with the national competitiveness perspective using a methodology based on Bayesian Causal Networks. First, we structure the competitiveness problem of the automotive industry through a synthesis of expert knowledge in the light of the World Economic Forum's competitiveness indicators. Second, we model the relationships among the variables identified in the problem structuring stage and analyse these relationships using a Bayesian Causal Network. Third, we develop policy suggestions under various scenarios to enhance the national competitive advantages of the automotive industry. We present an analysis of the Turkish automotive industry as a case study. It is possible to generalise the policy suggestions developed for the case of Turkish automotive industry to the automotive industries in other developing countries where country and industry competitiveness levels are similar to those of Turkey.

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Section: Industry Competitivenessmentioning

confidence: 99%

A decision support methodology to enhance the competitiveness of the Turkish automotive industry

Ülengin

Önsel

Aktaş

et al. 2014

European Journal of Operational Research

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“…Nasierowski and Arcelus (2003) [15] has applied the DEA to calculate efficiency of innovation system for over 40 countries and regions. Guan Jiancheng et al (2006) [10] also have done a lot of innovation efficiency calculation with DEA in both country and regional levels. An obvious advantage of DEA Approach is the simplification of indicator system since only indicators of innovation inputs and outputs are required.…”

Section: Quantitative Methods For Analysis Of Nismentioning

confidence: 99%

Factors Affecting the Efficiency of the BRICSs' National Innovation Systems: A Comparative Study Based on Dea and Panel Data Analysis

Cai

2011

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. (2) In accordance with the NIS approach and the new growth theory, there are many factors that affect the NISs including ICT infrastructure, enterprise R&D, market environment, governance, education systems, economic scale, natural endowments, and external dependence. (3) Enterprise innovation is of particular importance for the NISs. To improve the efficiency of innovation systems, efforts should be made to improve the market conditions, governance, and financial structures, and create a sound environment for R&D. (4) ICT infrastructure, economic scale, and openness affect the diffusion of knowledge and technology, and in turn affect NIS efficiency. (5) The BRICS have low governance levels and a high dependency on natural resources, both of which are determined by their stage of development and extensive growth patterns. To avoid the socalled middle-income trap, the BRICS should transform their factor-driven growth patterns into innovation-driven growth patterns. China still needs to improve its ICT infrastructure, its governance systems, and its education system. During its 12th five-year plan, more effort should be devoted to these fields and to improving external conditions for R&D. Terms of use: Documents inJEL O30, O57, P52

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“…The decision making units (DMUs) of studies on innovation efficiency have been diverse; from firm-level to province-level, even broader to the country-level [10,11,[25][26][27][28][29][30]. Table 1 summarized recent studies of innovation efficiency.…”

Section: Innovation Efficiencymentioning

confidence: 99%

“…First, we consider the input and output factors of previous literature to measure innovation efficiency. To analyze innovation efficiency precisely, we adopted the number of R&D employee and R&D expense as two input factors [11,[25][26][27][28][29][30]. Similarly, patent application and innovation sales are set as output factors [10,[25][26][27]30].…”

Section: Modelmentioning

confidence: 99%

“…Therefore, the innovation efficiency in this study could be an indicator of how sufficiently firms succeeded in innovation by comparing the input and output for innovation. Studies on innovation used DEA to measure innovation efficiency, as mentioned in Section 2, DEA is determined to be an appropriate method to measure innovation efficiency [10,11,[25][26][27][28][29][30].…”

Section: Data Envelopment Analysis and Tobit Regressionmentioning

confidence: 99%

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The Effect of Sustainability as Innovation Objectives on Innovation Efficiency

2018

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Though sustainability exists as major objective of pursuing innovation, the effect of it on innovation performance is still unanswered. While the outcome of innovation should consider both input and output, moreover, studies on sustainable innovation are yet to consider the overall efficiency, but rather the performance itself. To fill the gap between the effect of sustainability as the objective of innovation and its innovation performance, and to bridge the hole between the outcome of sustainable innovation and its efficiency, we analyze the relationship between sustainability as innovation objective and innovation efficiency. We use 441 manufacturing companies in Korea from 2016 KIS data, and perform the data envelopment analysis (DEA) method to calculate efficiency score of each firm and tobit regression analysis to investigate the effect of sustainability as an objective of innovation on innovation efficiency. The results show that the objective of 'environmental improvement' negatively affects innovation efficiency, while 'safety improvement' positively affects the efficiency. On the contrary, the effect of 'material and energy reduction' as an objective of innovation on innovation efficiency is not verified. The implications, limitations, and guidelines for future research are presented.

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A study of the relationship between competitiveness and technological innovation capability based on DEA models

Cited by 299 publications

References 39 publications

A decision support methodology to enhance the competitiveness of the Turkish automotive industry

A decision support methodology to enhance the competitiveness of the Turkish automotive industry

Factors Affecting the Efficiency of the BRICSs' National Innovation Systems: A Comparative Study Based on Dea and Panel Data Analysis

The Effect of Sustainability as Innovation Objectives on Innovation Efficiency

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