Based on dynamic capability theory and legitimacy theory,a theoretical model is constructed to examine how big data capability,through the mediation of knowledge dynamic capability,drive business model innovation unde...Based on dynamic capability theory and legitimacy theory,a theoretical model is constructed to examine how big data capability,through the mediation of knowledge dynamic capability,drive business model innovation under the moderation effect of innovation legitimacy.The eanalys is isconducted using regression analysis and fuzzy set qualitative comparative analysis(fsQCA)on survey data from 302 enterprises that have already implemented big data application practices.The study finds the following four conclusions.(1)Big data capability has a significant positive impact on business model innovation.(2)Dynamic knowledge capability partially mediates the relationship between big data capability and business model innovation.(3)Innovation legitimacy positively influences business model innovation and positively moderates the relationship between big data capability and businessmodel innovation.(4)Through further qualitative comparative analysis,two causal paths that influence business model innovation are identified.展开更多
With the expansion of the application range and network scale of wireless sensor networks in recent years,WSNs often generate data surges and delay queues during the transmission process,causing network paralysis,even...With the expansion of the application range and network scale of wireless sensor networks in recent years,WSNs often generate data surges and delay queues during the transmission process,causing network paralysis,even resulting in local or global congestion.In this paper,a dynamically Adjusted Duty Cycle for Optimized Congestion based on a real-time Queue Length(ADCOC)scheme is proposed.In order to improve the resource utilization rate of network nodes,we carried out optimization analysis based on the theory and applied it to the adjustment of the node’s duty cycle strategy.Using this strategy to ensure that the network lifetime remains the same,can minimize system delay and maximize energy efficiency.Firstly,the problems of the existing RED algorithm are analyzed.We introduce the improved SIG-RED algorithm into the ADCOC mechanism.As the data traffic changes,the RED protocol cannot automatically adjust the duty cycle.A scheduler is added to the buffer area manager,referring to a weighted index of network congestion,which can quickly determine the status of network congestion.The value of the weighting coefficient W is adjusted by the Bayesian method.The scheduler preferably transmits severely urgent data,alleviating the memory load.Then we combined improved data fusion technology and information gain methods to adjust the duty cycle dynamically.By simulating the algorithm,it shows that it has faster convergence speed and smaller queue jitter.Finally,we combine the adjusted congestion weight and the duty cycle growth value to adjust the data processing rate capability in the real-time network by dynamically adjusting it to adapt to bursts of data streams.Thus,the frequency of congestion is reduced to ensure that the system has higher processing efficiency and good adaptability.展开更多
Multimedia data have become popularly transmitted content in opportunistic networks. A large amount of video data easily leads to a low delivery ratio. Breaking up these big data into small pieces or fragments is a re...Multimedia data have become popularly transmitted content in opportunistic networks. A large amount of video data easily leads to a low delivery ratio. Breaking up these big data into small pieces or fragments is a reasonable option. The size of the fragments is critical to transmission efficiency and should be adaptable to the communication capability of a network. We propose a novel communication capacity calculation model of opportunistic network based on the classical random direction mobile model, define the restrain facts model of overhead, and present an optimal fragment size algorithm. We also design and evaluate the methods and algorithms with video data fragments disseminated in a simulated environment. Experiment results verified the effectiveness of the network capability and the optimal fragment methods.展开更多
In the digital economy era,many manufacturing enterprises are leveraging digital service enterprises to enhance their digital innovation processes.This paper introduces the concept of“digital innovation network embed...In the digital economy era,many manufacturing enterprises are leveraging digital service enterprises to enhance their digital innovation processes.This paper introduces the concept of“digital innovation network embeddedness”to describe this trend.Unlike traditional strategic resources,which are constrained by high-value,relatively static,restricted flow,and exclusivity,digital resources demonstrate superior fluidity,non-rivalrous access,and high value-driven interdependencies.To bridge this theoretical gap,this study examines the distinctive attributes of digital resources through the dual lenses of resource orchestration theory and dynamic capability theory.Specifically,it proposes big data capability as a mediating mechanism and organizational structure flexibility as a critical moderating factor.Developing this integrated research framework aims to elucidate the underlying mechanisms through which digital innovation network embeddedness dynamically shapes new product development(NPD)performance.Ultimately,this study seeks to advance theoretical understanding and provide actionable insights for digitally empowered manufacturing enterprises to enhance their NPD outcomes.The framework is tested using data from 559 manufacturing enterprises located in South China.There are three findings.(1)An inverted U-shaped relationship exists between digital innovation network structure and NPD performance,and between relationship embeddedness and NPD performance,respectively.(2)Big data capability mediates the relationship between moderate levels of digital innovation network embeddedness and NPD performance.However,at high levels of digital innovation network embeddedness,big data capability does not significantly mediate the relationship between digital innovation network structure/relationship embeddedness and NPD performance,respectively.(3)Organizational structure flexibility positively moderates the relationship between digital innovation network relationship embeddedness,big data capability,and NPD performance.Moreover,while mediated moderation occurred,the direct moderation effect on digital innovation network embeddedness is nonsignificant.The conclusions of this study provide insights for manufacturing enterprises seeking to enhance NPD performance within the context of digital innovation network embeddedness.展开更多
基金general project(No.71672080,72072086)of the National Natural ScienceFoundation of China.
文摘Based on dynamic capability theory and legitimacy theory,a theoretical model is constructed to examine how big data capability,through the mediation of knowledge dynamic capability,drive business model innovation under the moderation effect of innovation legitimacy.The eanalys is isconducted using regression analysis and fuzzy set qualitative comparative analysis(fsQCA)on survey data from 302 enterprises that have already implemented big data application practices.The study finds the following four conclusions.(1)Big data capability has a significant positive impact on business model innovation.(2)Dynamic knowledge capability partially mediates the relationship between big data capability and business model innovation.(3)Innovation legitimacy positively influences business model innovation and positively moderates the relationship between big data capability and businessmodel innovation.(4)Through further qualitative comparative analysis,two causal paths that influence business model innovation are identified.
基金This work is supported by“National Science Foundation of Hunan Province,China”under Grant 2020JJ4757.
文摘With the expansion of the application range and network scale of wireless sensor networks in recent years,WSNs often generate data surges and delay queues during the transmission process,causing network paralysis,even resulting in local or global congestion.In this paper,a dynamically Adjusted Duty Cycle for Optimized Congestion based on a real-time Queue Length(ADCOC)scheme is proposed.In order to improve the resource utilization rate of network nodes,we carried out optimization analysis based on the theory and applied it to the adjustment of the node’s duty cycle strategy.Using this strategy to ensure that the network lifetime remains the same,can minimize system delay and maximize energy efficiency.Firstly,the problems of the existing RED algorithm are analyzed.We introduce the improved SIG-RED algorithm into the ADCOC mechanism.As the data traffic changes,the RED protocol cannot automatically adjust the duty cycle.A scheduler is added to the buffer area manager,referring to a weighted index of network congestion,which can quickly determine the status of network congestion.The value of the weighting coefficient W is adjusted by the Bayesian method.The scheduler preferably transmits severely urgent data,alleviating the memory load.Then we combined improved data fusion technology and information gain methods to adjust the duty cycle dynamically.By simulating the algorithm,it shows that it has faster convergence speed and smaller queue jitter.Finally,we combine the adjusted congestion weight and the duty cycle growth value to adjust the data processing rate capability in the real-time network by dynamically adjusting it to adapt to bursts of data streams.Thus,the frequency of congestion is reduced to ensure that the system has higher processing efficiency and good adaptability.
基金supported by the Shaanxi Natural Science Foundation Research Plan (No. 2015JQ6238)the China Scholarship Council+3 种基金the National Natural Science Foundation of China(Nos. 61373083 and 61402273)the Fundamental Research Funds for the Central Universities of China (No. GK201401002)the Program of Shaanxi Science and Technology Innovation Team of China (No. 2014KTC18)the 111 Programme of Introducing Talents of Discipline to Universities (No. B16031)
文摘Multimedia data have become popularly transmitted content in opportunistic networks. A large amount of video data easily leads to a low delivery ratio. Breaking up these big data into small pieces or fragments is a reasonable option. The size of the fragments is critical to transmission efficiency and should be adaptable to the communication capability of a network. We propose a novel communication capacity calculation model of opportunistic network based on the classical random direction mobile model, define the restrain facts model of overhead, and present an optimal fragment size algorithm. We also design and evaluate the methods and algorithms with video data fragments disseminated in a simulated environment. Experiment results verified the effectiveness of the network capability and the optimal fragment methods.
基金supported by the Post-Funded Project of the National Social Science Fund of China(No.23FGLB088)the General Project of the National Natural Science Foundation of China(No.71974059)+2 种基金the Ministry of Education in China Liberal Arts and Social Sciences Foundation(No.23YJA630124)the Development of Philosophy and Social Sciences in Guangzhou in 2021(No.2021GZYB12)the Guangdong Basic and Applied Basic Research Foundation(No.2024A1515030110).
文摘In the digital economy era,many manufacturing enterprises are leveraging digital service enterprises to enhance their digital innovation processes.This paper introduces the concept of“digital innovation network embeddedness”to describe this trend.Unlike traditional strategic resources,which are constrained by high-value,relatively static,restricted flow,and exclusivity,digital resources demonstrate superior fluidity,non-rivalrous access,and high value-driven interdependencies.To bridge this theoretical gap,this study examines the distinctive attributes of digital resources through the dual lenses of resource orchestration theory and dynamic capability theory.Specifically,it proposes big data capability as a mediating mechanism and organizational structure flexibility as a critical moderating factor.Developing this integrated research framework aims to elucidate the underlying mechanisms through which digital innovation network embeddedness dynamically shapes new product development(NPD)performance.Ultimately,this study seeks to advance theoretical understanding and provide actionable insights for digitally empowered manufacturing enterprises to enhance their NPD outcomes.The framework is tested using data from 559 manufacturing enterprises located in South China.There are three findings.(1)An inverted U-shaped relationship exists between digital innovation network structure and NPD performance,and between relationship embeddedness and NPD performance,respectively.(2)Big data capability mediates the relationship between moderate levels of digital innovation network embeddedness and NPD performance.However,at high levels of digital innovation network embeddedness,big data capability does not significantly mediate the relationship between digital innovation network structure/relationship embeddedness and NPD performance,respectively.(3)Organizational structure flexibility positively moderates the relationship between digital innovation network relationship embeddedness,big data capability,and NPD performance.Moreover,while mediated moderation occurred,the direct moderation effect on digital innovation network embeddedness is nonsignificant.The conclusions of this study provide insights for manufacturing enterprises seeking to enhance NPD performance within the context of digital innovation network embeddedness.
