Using the data mining method to assess the innovation gap: A case of industrial robotics in a catching-up country

Kong, Delong; Zhou, Yuan; Liu, Yufei; Xue, Lan

doi:10.1016/j.techfore.2017.02.035

Cited by 56 publications

(23 citation statements)

References 78 publications

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“…Using patents as innovation indicators has several advantages [26,[73][74][75]: patent data is objective, because it has been processed and acknowledged by patent examiners; patent data is publicly accessible; and patent data contains detailed information regarding the technologies. In our research, we used patent application data rather than patent grant data to measure innovation.…”

Section: Sample and Datamentioning

confidence: 99%

The Impact of Corporate Social Responsibility on Firms’ Innovation in China: The Role of Institutional Support

Zhou

et al. 2019

Sustainability

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Corporate Social Responsibility (CSR) literature in developed countries indicates that there are two types of CSR (i.e., proactive CSR and reactive CSR), and only the proactive one can boost innovation. However, recent studies from emerging economies such as China show that both types of CSR can enhance innovation. Such inconsistent results may be created by, on the one hand, the heterogeneity of innovation types, and on the other hand, the heterogeneity of mechanisms though which CSR impacts innovation in different countries. Accordingly, this paper theoretically explores the impacts of two types of CSR (i.e., proactive CSR and reactive CSR) on two of innovation types (exploratory innovation and exploitative innovation). This paper further reveals the mechanisms through which CSR impacts innovation in China by investigating the moderating role of institutional support (i.e., government support and social support) on the relationship between CSR and innovation. Using a panel dataset (2008–2016) of 286 Chinese public listed firms from research and development (R&D)-intensive industries (e.g., information technology, pharmaceutical and biological products; and chemicals, etc.), our findings show that: (1) proactive CSR promotes exploratory innovation; (2) reactive CSR promotes exploitative innovation; (3) government support strengthen the relationship between proactive CSR and exploratory innovation as well as the relationship between reactive CSR and exploitative innovation; (4) social support weaken the relationship between proactive CSR and exploratory innovation. This paper enriches our understanding on the relationship between CSR and innovation, and provides implications for practitioners and policymakers.

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Section: Sample and Datamentioning

confidence: 99%

The Impact of Corporate Social Responsibility on Firms’ Innovation in China: The Role of Institutional Support

Zhou

et al. 2019

Sustainability

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“…1). The general process of constructing the classifier mainly has three steps: labeling samples, constructing data sets, and training and testing classifiers (Fu and Aliferis 2010;Kong et al 2017;Lee et al 2018).…”

Section: Forecasting Et Based Supervised Learningmentioning

confidence: 99%

Forecasting emerging technologies using data augmentation and deep learning

et al. 2020

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Deep learning can be used to forecast emerging technologies based on patent data. However, it requires a large amount of labeled patent data as a training set, which is difficult to obtain due to various constraints. This study proposes a novel approach that integrates data augmentation and deep learning methods, which overcome the problem of lacking training samples when applying deep learning to forecast emerging technologies. First, a sample data set was constructed using Gartner's hype cycle and multiple patent features. Second, a generative adversarial network was used to generate many synthetic samples (data augmentation) to expand the scale of the sample data set. Finally, a deep neural network classifier was trained with the augmented data set to forecast emerging technologies, and it could predict up to 77% of the emerging technologies in a given year with high precision. This approach was used to forecast emerging technologies in Gartner's hype cycles for 2017 based on patent data from 2000 to 2016. Four out of six of the emerging technologies were forecasted correctly, showing the accuracy and precision of the proposed approach. This approach enables deep learning to forecast emerging technologies with limited training samples.

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“…If senior managers responsible for innovation activities lack enthusiasm, it will affect the implementation of innovation work, and the innovation ability of them will not be effectively exerted. Therefore, through appropriate incentives for employees, it can increase employees' awareness of innovation investment and promote the technological innovation of enterprises, thereby maintaining the long-term stable development of enterprises in market competition (Fang and Shi, 2016;Kong et al, 2017). A systematic and dynamic salary system can ensure the orderly governance of the enterprise so that employees can gain their value and benefits while developing as much as possible, thereby motivating employees to maximize their enthusiasm in their daily work.…”

Section: The Relationship Between Employee Salary Gap and Enterprise mentioning

confidence: 99%

The Correlation Analysis Between Salary Gap and Enterprise Innovation Efficiency Based on the Entrepreneur Psychology

et al. 2020

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Using the data mining method to assess the innovation gap: A case of industrial robotics in a catching-up country

Cited by 56 publications

References 78 publications

The Impact of Corporate Social Responsibility on Firms’ Innovation in China: The Role of Institutional Support

The Impact of Corporate Social Responsibility on Firms’ Innovation in China: The Role of Institutional Support

Forecasting emerging technologies using data augmentation and deep learning

The Correlation Analysis Between Salary Gap and Enterprise Innovation Efficiency Based on the Entrepreneur Psychology

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