Measuring the innovation production process: A cross-region empirical study of China’s high-tech innovations

Guan, Jiancheng; Chen, Kaihua

doi:10.1016/j.technovation.2010.02.001

Cited by 283 publications

(170 citation statements)

References 66 publications

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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%

“…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%

“…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%

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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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Section: Innovation Efficiencymentioning

confidence: 99%

Section: Data Envelopment Analysis and Tobit Regressionmentioning

confidence: 99%

Section: Modelmentioning

confidence: 99%

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The Effect of Sustainability as Innovation Objectives on Innovation Efficiency

2018

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“…2003]. Na poziomie makroekonomicznym pod wagę brano również [Guan, Chen 2010] naukę, badania i rozwój, produkcję, marketing i organizacje jako wejścia, a udział w rynku, wzrost sprzedaży, tempo eksportu, zmianę produktywności jako wyjścia. W obu jednak przypadkach pod uwagę na poziomie wyjścia bierzemy czynniki zależne również w dużej mierze od czynników niezwią-zanych z działalnością innowacyjną.…”

Section: Wstępunclassified

Efektywność innowacyjna systemu triple-helix w Unii Europejskiej / The effectiveness of an innovative triple-helix system in the European Union

Szajt¹

2016

Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu

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“…With respect to R&D, many previous studies have explained that R&D is not an isolated single stage, but at least two stages forming a process [39][40][41][42]. A two-stage process is considered a more appropriate classification for R&D because it includes an innovation stage and a commercialization stage [19].…”

Section: Two-stage Dea Model For Analyzing Innovation and Commercialimentioning

confidence: 99%

Labor Union Effects on Innovation and Commercialization Productivity: An Integrated Propensity Score Matching and Two-Stage Data Envelopment Analysis

et al. 2015

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Abstract:Research and development (R&D) is a critical factor in sustaining a firm's competitive advantage. Accurate measurement of R&D productivity and investigation of its influencing factors are of value for R&D productivity improvements. This study is divided into two sections. The first section outlines the innovation and commercialization stages of firm-level R&D activities. This section analyzes the productivity of each stage using a propensity score matching (PSM) and two-stage data envelopment analysis (DEA) integrated model to solve the selection bias problem. Second, this study conducts a comparative analysis among subgroups categorized as labor unionized or non-labor unionized on productivity at each stage. We used Korea Innovation Survey (KIS) data for analysis using a sample of 400 Korean manufacturers. The key findings of this study include: (1) firm innovation and commercialization productivity are balanced and show relatively low innovation productivity; and (2) labor unions have a positive effect on commercialization productivity. Moreover, labor unions are an influential factor in determining manufacturing firms' commercialization productivity. OPEN ACCESSSustainability 2015, 7 5121

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Measuring the innovation production process: A cross-region empirical study of China’s high-tech innovations

Cited by 283 publications

References 66 publications

The Effect of Sustainability as Innovation Objectives on Innovation Efficiency

The Effect of Sustainability as Innovation Objectives on Innovation Efficiency

Efektywność innowacyjna systemu triple-helix w Unii Europejskiej / The effectiveness of an innovative triple-helix system in the European Union

Labor Union Effects on Innovation and Commercialization Productivity: An Integrated Propensity Score Matching and Two-Stage Data Envelopment Analysis

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