Innovation efficiency in European high-tech industries: Evidence from a Bayesian stochastic frontier approach

Haschka, Rouven E.; Herwartz, Helmut

doi:10.1016/j.respol.2020.104054

Cited by 47 publications

(14 citation statements)

References 83 publications

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“…Li et al [11] proposes a new framework based on the combination of the dynamic DEA to study the innovation efficiency of China's high-tech industries, and the results show that the eastern region leads and the central and western regions are relatively lagging behind. In recent years, Haschka et al used the Bayesian stochastic frontier approach to measure the innovation efficiency values of European high-tech industries [12]. Chen et al [13] used the DEA method to measure the innovation efficiency of Chinese high-tech industries.…”

Section: The Measurement Of Innovation Efficiencymentioning

confidence: 99%

Measurement of Innovation Efficiency in China’s Electronics and Communication Equipment Manufacturing Industry-Based on Dynamic Network SBM Model

2022

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In recent years, China’s electronics and communication equipment manufacturing (ECEM) industry has overgrown, and the government should assess the innovation performance of the industry for its sustainable development. However, most previous studies on the innovation efficiency of the ECEM industry have ignored the link and carry-over variables. This paper uses the number of patent applications as a link variable to consider the stage of innovation activities. It divides the innovation activities of the electronics industry into two stages: technology development and results in transformation. To consider the dynamics of innovation activities, this paper uses capital stock as a period carry-over variable and evaluates the change of innovation efficiency over time. In this paper, the DNSBM model is used to measure the innovation efficiency of the ECEM industry in 26 Chinese provinces from 2013–2019. This model includes both stage link variables and period carry-over variables, thus allowing for overall efficiency and stage efficiency and period efficiency. The results show that the overall innovation efficiency values in the Chinese ECEM industry are low, there are considerable differences between the two-stage efficiency values in the east, central and western regions, and the overall efficiency values show a slow upward trend.

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Section: The Measurement Of Innovation Efficiencymentioning

confidence: 99%

Measurement of Innovation Efficiency in China’s Electronics and Communication Equipment Manufacturing Industry-Based on Dynamic Network SBM Model

2022

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“…In (),

\,{\text{latent knowledge}}_{i}

denotes the knowledge generated from a vector of innovation inputs

{X}_{i}

subject to a production function

f({X}_{i};\beta )

, for example, of Cobb–Douglas type (Haschka & Herwartz, 2020; Siebert, 2017), and technical efficiency

T{E}_{i}\in [0,1]

. Both knowledge and technical efficiency are known to the firm, but unobserved by the analyst.…”

Section: Latent Knowledge Generation and Iv‐free Estimationmentioning

confidence: 99%

“…Finally, given that drug development touches sensitive societal fields, such as public health and health policy, both the composition and experience of the research staff could hold particular importance for effective knowledge generation in the pharmaceutical industry in comparison with other sectors. Going beyond endogeneity concerns, it is worth noting that for this industry research taking place in universities—that is, pharmaceutical faculties—could act as a major source of knowledge transfer and the acquisition of key business skills (Haschka & Herwartz, 2020; Powell, 1998). Hence, the description of knowledge generation subject to intraindustry competition is likely to benefit from the potential to scale inefficiencies in SFA models, as in H.‐J.…”

Section: Introductionmentioning

confidence: 99%

Endogeneity in pharmaceutical knowledge generation: An instrument‐free copula approach for Poisson frontier models

Haschka

Herwartz

2022

Economics Manag Strategy

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This study provides an assessment of the R&D-patent relation of European pharmaceutical firms that are not flawed by endogeneity biases. Firms invest in R&D and generate latent knowledge which then manifests in observable patent outcomes through a Poisson model. The process of turning R&D into knowledge is described by a production process subject to inefficiency and endogeneity. To estimate a Poisson stochastic frontier model, the suggested novel copula-based approach directly accounts for the dependence between the endogenous regressors and the inefficiency component. Hence, its implementation does not require any instrumental variables. Simulation results underline that the proposed estimator outperforms conventional instrumental variable estimators. Neglecting endogeneity leads to a substantial underestimation of the R&D elasticity of patents generated in the European pharmaceutical industry.

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“…Second, with the competitive advantage of technological innovation, products manufactured by high-tech industries should be highly efficient. Thus, HTID promotes a low-carbon lifestyle among the adopters of high-tech products (Haschka, & Herwartz, 2020), such as solar water heaters and new energy vehicles. Therefore, energy efficiency is improved through advanced technologies.…”

Section: Htid and Energy Efficiencymentioning

confidence: 99%

The impact of high-tech industry development on energy efficiency and its influencing mechanisms

Yanfen

Huang

Xiang

et al. 2022

Front. Environ. Sci.

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In responding to climate change, energy efficiency is one of the key factors for sustainable development, where the high-tech industry can play an important role. However, whether the development of high-tech industry impacts energy efficiency as well as the mechanisms behind still remain unclear. Thus, based on the dynamic spatial Durbin model, this study aims to investigate: 1) the impact of high-tech industry development on energy efficiency from three perspectives of high-tech industry development, i.e., scale, productivity, and agglomeration, and 2) the mechanisms behind such impact especially through technological innovation and industrial structure. The results confirm the influence of high-tech industry development on energy efficiency both directly and indirectly. On the one hand, our analysis contributes on the existing body of scientific knowledge by expounding the relationship between scale, productivity, and agglomeration of high-tech industry development and energy efficiency. On the other hand, it further deepens the understanding on such relationship by revealing two underlying mechanisms behind, i.e., through promoting technological innovation, the productivity and agglomeration of high-tech industries can either completely or partially improve energy efficiency, while the scale and agglomeration of high-tech industries can hinder energy efficiency to a certain level through the industrial restructuring. Based on these findings, this paper provides some policy implications, which are believed to facilitate the practices of energy conservation and emission reduction in China.

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Innovation efficiency in European high-tech industries: Evidence from a Bayesian stochastic frontier approach

Cited by 47 publications

References 83 publications

Measurement of Innovation Efficiency in China’s Electronics and Communication Equipment Manufacturing Industry-Based on Dynamic Network SBM Model

Measurement of Innovation Efficiency in China’s Electronics and Communication Equipment Manufacturing Industry-Based on Dynamic Network SBM Model

Endogeneity in pharmaceutical knowledge generation: An instrument‐free copula approach for Poisson frontier models

The impact of high-tech industry development on energy efficiency and its influencing mechanisms

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