Research on the Influence of Knowledge Base and Relationship Network on Innovation Performance

Wang, Hui; Zhang, Xiaoming; Li-ren, AN

doi:10.26480/aim.02.2018.01.03

Cited by 4 publications

(2 citation statements)

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“…Strong-memory strategic decision-makers can accurately repeat valuable knowledge, enhancing decision-making efficiency and degree ( Fechner et al, 2016 ). The large and deep network of information may help or traumatize employees’ sustainable innovation performance ( Wang et al, 2018 ; Wiseman et al, 2022 ), which becomes part of their memory. A good memory can provide strategic decision-making concepts, processes, approaches, insights, and lessons ( Bechara and Martin, 2004 ).…”

Section: Literature Review and Hypotheses Developmentmentioning

confidence: 99%

Identifying the factors affecting strategic decision-making ability to boost the entrepreneurial performance: A hybrid structural equation modeling – artificial neural network approach

et al. 2022

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This study builds a conceptual model of strategic decision-making ability that leads to entrepreneurial performance (EP) based on the two-system decision-making theory and logical analysis. An empirical approach using structural equation modeling – artificial neural network (SEM-ANN) was performed to describe the linear and nonlinear relationships in the proposed model. The empirical results reveal that strategic decision-making abilities are affected by five factors: attention, memory, thinking, emotion, and sentiment, and whose influence mechanisms and degrees are varied. Results also describe that these abilities have a positive effect on overall EP. Therefore, results suggest that businesses’ strategic decision-making is usually strengthened when entrepreneurs have a clear understanding of these influencing elements, and the interaction between them leads to improved performance.

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Section: Literature Review and Hypotheses Developmentmentioning

confidence: 99%

Identifying the factors affecting strategic decision-making ability to boost the entrepreneurial performance: A hybrid structural equation modeling – artificial neural network approach

et al. 2022

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show abstract

“…Due to cognitive limitations, enterprises can only consider a small part of the knowledge domain, defined as the enterprise knowledge base (Yayavaram and Chen, 2015), in the process of exploring and reorganising technological knowledge. Studies in the fields of technology and innovation management often discuss the knowledge base in terms of its width and depth (Wang et al , 2018; Rui and Lyytinen, 2019). Knowledge base width (KBW) relates to technological diversity (Wijk et al , 2012) that helps enterprises to innovate and upgrade their technological level (Henderson and Cockbum, 1994; Phelps, 2010).…”

Section: Theoretical Framework and Hypothesesmentioning

confidence: 99%

Knowledge base, technology gap and technology development speed in technological catch-up: innovation choice for China's knowledge-intensive enterprises (KIEs)

Zhang

Jin

Wen

et al. 2021

EJIM

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PurposeWith the fierce competition in a knowledge economy, knowledge-intensive enterprises (KIEs) make technological progress in their catching-up processes through implementing product innovation and process innovation. In this study, the aim is to understand the determinants of enterprise innovation type in China's catch-up environment. Further, this paper intends to deal with two related questions. First, what effect does the internal knowledge base have on KIEs' technology innovation activities? Second, considering the technology gap and technology development speed, what are the different impacts of the knowledge base on the type of technology innovation activities?Design/methodology/approachThis paper collected data from 212 KIEs in China through a two-stage questionnaire survey, combined with statistical data for research. The hypothesis was tested by regression analysis. Specifically, descriptive statistics and regression analysis are introduced to test the hypothetical relationship between the knowledge base and technology innovation. Meanwhile, multiple regression is used to test whether there is any difference in the influence of technology gap and technology development speed on enterprise knowledge base and technology innovation. Finally, the corresponding robustness tests are done.FindingsThis study finds that in a sample of Yangtze River Delta KIEs, firms' knowledge base influences innovation types. Specifically, the knowledge base width (KBW) and knowledge base depth (KBD) positively influence process innovation, and KBD positively affects product innovation. Regarding the effects of catch-up context factors on KIEs’ innovation choice, a wide technology gap tends to positively influence product innovation in industries with high levels of KBW. Moreover, when technology development speed is high, its potential positive influence on process innovation will be more significant for industries with deeper knowledge bases.Originality/valueThis paper fills the research gap that existing studies ignore the relationship between types of technology innovation and knowledge base dimensions, especially for KIEs. First, this paper deepens the understanding of the impact mechanism of KIEs' existing knowledge base on innovation activities; the unique use of resources by enterprises is the basis of enterprises' competitive advantage and will become enterprises' competitive advantage. Second, this study indicates that against different backdrops of technology gap and technology development speed, enterprises with different knowledge bases will adopt different types of technology innovation activities. Third, this paper shows that a wider technology gap provides broader innovation space, so the technology gap plays a pulling role in KBW and product innovation, thus pushing forward enterprises' technological catch-up.

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Innovation of Enterprise Management Mode in the Era of Big Data

Zhang

Shi

2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Research on the Influence of Knowledge Base and Relationship Network on Innovation Performance

Cited by 4 publications

References 2 publications

Identifying the factors affecting strategic decision-making ability to boost the entrepreneurial performance: A hybrid structural equation modeling – artificial neural network approach

Identifying the factors affecting strategic decision-making ability to boost the entrepreneurial performance: A hybrid structural equation modeling – artificial neural network approach

Knowledge base, technology gap and technology development speed in technological catch-up: innovation choice for China's knowledge-intensive enterprises (KIEs)

Innovation of Enterprise Management Mode in the Era of Big Data

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