A multilevel and multistage efficiency evaluation of innovation systems: A multiobjective DEA approach

Carayannis, Elias G.; Grigoroudis, Evangelos; Goletsis, Yorgos

doi:10.1016/j.eswa.2016.06.017

Cited by 138 publications

(76 citation statements)

References 62 publications

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“…The measurement of production efficiency can also be used in stochastic frontier analysis or the FDH (free disposable hull) method. The study of Carayannis et al (2016) presented and confirmed DEA modelling as the most prominent method for combining the innovation factors and calculation of NIS effectiveness.…”

Section: Literature Review On Innovation Efficiencymentioning

confidence: 57%

Efficiency measurement of national innovation systems of the European Union countries: DEA model application

Pilík¹,

Kwarteng²

2019

Journal of International Studies

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This paper focuses on measuring technical efficiency of National Innovation System across a sample of the European Union (EU) countries using data envelopment analysis. We used an output-oriented constant returns to scale model to calculate the efficiency of the units represented by the European Union countries. This nonparametric method measures efficiency of input utilisation as compared to the achieved outputs during the consumption process. Following previous studies on this topic, we have used the number of researchers and the expenditures on research and development (R&D) as inputs and published scientific journal articles and applied patents as outputs of the model. Stemming from the available data, we have covered the period of 2005-2016. This period covers the economic period before the financial crisis and economic recession in which it was necessary to use resources effectively for the maximisation of final production. The study also presents data representing the era of growth trend in European economy. The only efficient countries in our study as of 2016 were found to be Cyprus, Luxembourg, Malta, and Romania. The number of efficient units decreased from six countries measured in 2005 from among the eight calculated in the years of the economic crisis to four efficient decision-making units (DMUs) in 2015. Germany, the best performer in the European Union with respect to patent rankings, was classified

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Section: Literature Review On Innovation Efficiencymentioning

confidence: 57%

Efficiency measurement of national innovation systems of the European Union countries: DEA model application

Pilík¹,

Kwarteng²

2019

Journal of International Studies

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“…Han, Asmild, and Kunc [6] evaluated the R&D efficiency patterns of 15 Korean regions and classified the regions into deteriorating, lagging, and improving groups. Carayannis et al [100] integrated an assessment and classification framework for national and regional innovation efficiency based on a set of 23 European countries and their 185 corresponding regions, and discovered large innovation efficiency differences. Wang et al [9] explored the environmental components of regional innovation efficiency in China, including economic infrastructure, the quality and structure of innovators, and regional openness, and found a chain structure relationship between regional innovation environmental components and innovation efficiency.…”

Section: Regional Sustainable Innovation Efficiency Evaluationmentioning

confidence: 99%

Efficiency Evaluation of Regional Sustainable Innovation in China: A Slack-Based Measure (SBM) Model with Undesirable Outputs

Bossink

Chen

2019

Sustainability

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An efficiency evaluation of China's regional sustainable innovation, evaluating industrial waste and total energy consumption, is the main research subject in this paper. It focuses on a regional measurement and comparison of these undesirable outputs of Chinese firm activities, such as industrial SO 2 and CO 2 emissions. By applying a data envelopment analysis-slack-based measure (DEA-SBM) model with undesirable outputs indicators, the regional innovation efficiency was evaluated for 30 provinces in China, from 2002 to 2014. The results indicate that the sustainable innovation efficiency of overall China is still relatively low, and varies significantly in different regions. Central and Western China have similar sustainable innovation efficiencies, which are much lower than the sustainable innovation efficiency in Eastern China. Furthermore, the data indicate that regional sustainable innovation efficiency disparities among these three areas are decreasing. Based on these findings, reasons for the sustainable innovation efficiency gap among the different regions were analyzed. To scholars, this paper extends the research on regional sustainable innovation efficiency by implementing an undesirable output perspective to the DEA-SBM model. The findings also provide Chinese policy makers with useful decision support insights for regional sustainable innovation, and energy conservation and emission reduction policies.Sustainability 2020, 12, 31 2 of 21 differences can be addressed [15][16][17]. Research on regional innovation efficiency has gained increasing attention in recent years [18][19][20], but to the best of our knowledge, very few studies have been conducted to quantitatively evaluate sustainable innovation efficiency in China, by means of focusing on undesirable outputs like industrial SO 2 and CO 2 emissions [21,22]. Our study aims to contribute to the knowledge in this area, and does this by means of measuring and comparing unsustainable, unwanted, and undesirable outcomes/byproducts of Chinese regions' innovative firm activities. By evaluating the regional sustainable innovation efficiency of 30 provinces in China from 2002 to 2014, using the slack-based measure (SBM) model, this study focuses on the following research question: what are the differences between sustainable innovation efficiencies of different regions in China and which trends in these differences can be estimated?How to deal with SO 2 and CO 2 emissions and with related climate change has become one of the most urgent challenges of today's world. For example, it has been reported that global CO 2 emissions from fuel combustion has increased from 26,177 Mt in 2004 to 32,190 Mt in 2013. Many countries have committed to controlling SO 2 and CO 2 emissions and aim to develop a no-to low-emissions economy. In 2015, the Paris Climate Change Conference adopted the "Paris Agreement" to deal with climate change after 2020 [24]. More than 170 countries jointly signed the Paris Agreement in 2016. China and the United States officially ratified the...

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“…It is a non-parametric mathematical programming method using linear programming and convex analysis as tools to calculate the relative efficiency between the evaluated units [33]. Considering that there exist multiple inputs and outputs in a real production situation, the DEA method, which is capable of offering a comprehensive optimal input-output scheme out of the decision-making unit, is especially appropriate for measuring innovation efficiency [34,35].…”

Section: Dependent Variablesmentioning

confidence: 99%

How Supply Chain Integration Affects Innovation in a Digital Age: Moderating Effects of Sustainable Policy

Chin

et al. 2019

Sustainability

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With the rapid development of digital technologies and increasing public attention on environmental problems, it has become a new challenge for global enterprises to manage supply chains responsibly, so as to improve their innovation performance for sustainability. Prior works have identified the effects of supply chain integration on firm innovation in advanced economies; however, so far, there has been limited research on the relationships between supply chain integration and firm innovation in emerging and developing countries. Hence, building upon transaction cost and resource dependence theories, this study used China’s manufacturing industry as the research setting, probing the dynamic mechanisms between supply chain integration and firm innovation. The results show that the degree of supply chain integration positively relates to firms’ patent output but negatively relates to their innovation efficiency, and that a sustainable policy moderates the foregoing associations. Our study enriches the body of knowledge regarding responsible supply chain integration in a new digital age with growing ecological concerns and thereby offers insightful practical implications for practitioners and policy makers.

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A multilevel and multistage efficiency evaluation of innovation systems: A multiobjective DEA approach

Cited by 138 publications

References 62 publications

Efficiency measurement of national innovation systems of the European Union countries: DEA model application

Efficiency measurement of national innovation systems of the European Union countries: DEA model application

Efficiency Evaluation of Regional Sustainable Innovation in China: A Slack-Based Measure (SBM) Model with Undesirable Outputs

How Supply Chain Integration Affects Innovation in a Digital Age: Moderating Effects of Sustainable Policy

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