2015
DOI: 10.1016/j.techfore.2014.04.007
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A structured approach to explore knowledge flows through technology-based business methods by integrating patent citation analysis and text mining

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Cited by 53 publications
(26 citation statements)
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References 26 publications
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“…The division of knowledge flow has not yet been agreed upon. [32] analyzes and evaluates the effect of knowledge flow in networks from two dimensions: knowledge dissemination and transmission rates. Lin (2006) [33] uses the network knowledge map to divide knowledge flow into knowledge acquisition, externalization, storage, sharing, and innovation.…”
Section: The Influence Of Cooperative Culture On Governance Performanmentioning
confidence: 99%
“…The division of knowledge flow has not yet been agreed upon. [32] analyzes and evaluates the effect of knowledge flow in networks from two dimensions: knowledge dissemination and transmission rates. Lin (2006) [33] uses the network knowledge map to divide knowledge flow into knowledge acquisition, externalization, storage, sharing, and innovation.…”
Section: The Influence Of Cooperative Culture On Governance Performanmentioning
confidence: 99%
“…Also, the data and information produced through the IoTs is used to generate knowledge that is ultimately employed to monitor and control business network operations. The companies that utilize the links between the IoTs and dynamic data and information processing capabilities (DIDPCs) achieve a better competitive advantage; they do so because their daily business operations become more agile as a result of these developments in their IT infrastructures (Christopher, 2000;Heisterberg and Verma, 2014;Lou et al, 2011;No et al, 2015). The significance of the IoTs and of its linkages with operational effectiveness, data, and informed decision making has been highlighted by key IT players.…”
Section: Introductionmentioning
confidence: 99%
“…More specifically, the linkages between the use of the IoTs and DIDPCs to generate knowledge suitable to improve operational agility have neither been effectively researched nor statistically estimated. Above all, the field is still devoid of clear theoretical bases (Bresciani et al, 2017;Cram and Newell, 2016;No et al, 2015;Teece, 2014a).…”
Section: Introductionmentioning
confidence: 99%
“…Jaffeused the sales revenue of each product category to measure the proximity of technology positioning among enterprises [26]. Kwon et al and No HJ et al proposed concepts such as similarity, proximity, and distance of industry or enterprise, which can be improved to compare technology development structures [27,28]. Caviggioli proposed a technical distance measurement indicator-a reverse concept of technical similarity, which combines International Patent Classification (IPC)1, International Patent Classification(IPC)3, and World Intellectual Property Organization (WIPO) technology categories [29].…”
Section: Literature Reviewmentioning
confidence: 99%