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個(gè)體在線知識(shí)分享行為的慣性效應(yīng)分析

發(fā)布時(shí)間:2018-11-06 14:30
【摘要】:在線知識(shí)社群是大數(shù)據(jù)時(shí)代組織學(xué)習(xí)、協(xié)同創(chuàng)新、知識(shí)與信息交流與轉(zhuǎn)移的重要方式。針對(duì)這種在線知識(shí)網(wǎng)絡(luò)的分析、預(yù)測和管控,是大數(shù)據(jù)對(duì)經(jīng)濟(jì)社會(huì)實(shí)現(xiàn)數(shù)據(jù)服務(wù)和決策咨詢功能的重要環(huán)節(jié)。以CMKT咨詢俱樂部2群QQ群為例,搜集分析該群在2016年3月份所有的專業(yè)交流、討論和分享記錄,并基于這些實(shí)時(shí)數(shù)據(jù)建構(gòu)CMKT動(dòng)態(tài)知識(shí)網(wǎng)絡(luò),分析了在線動(dòng)態(tài)知識(shí)網(wǎng)絡(luò)中個(gè)體知識(shí)分享行為的策略性選擇,最終驗(yàn)證了動(dòng)態(tài)知識(shí)網(wǎng)絡(luò)中個(gè)體分享行為的慣性效應(yīng);谶@一慣性效應(yīng),在大數(shù)據(jù)技術(shù)的支撐下,政府、高;蚩蒲袡C(jī)構(gòu)以及大型企業(yè)可以對(duì)其所構(gòu)建的跨組織學(xué)習(xí)知識(shí)網(wǎng)絡(luò)的動(dòng)態(tài)演化趨勢進(jìn)行有效的分析和預(yù)測,并適時(shí)地選擇干預(yù)或管控手段以引導(dǎo)知識(shí)網(wǎng)絡(luò)更好地發(fā)揮在協(xié)同創(chuàng)新、知識(shí)轉(zhuǎn)移、跨組織學(xué)習(xí)等方面的作用。
[Abstract]:Online knowledge community is an important way to organize learning, collaborative innovation, knowledge and information exchange and transfer in big data's time. The analysis, prediction and control of this online knowledge network is an important link for big data to realize the function of data service and decision-making consultation to the economy and society. Taking CMKT Consulting Club Group 2 as an example, this paper collects and analyzes all the professional exchanges, discussions and records of this group in March 2016, and constructs a CMKT dynamic knowledge network based on these real-time data. The strategic choice of individual knowledge sharing behavior in online dynamic knowledge network is analyzed and the inertia effect of individual sharing behavior in dynamic knowledge network is verified. Based on this inertia effect, under the support of big data technology, the government, universities or scientific research institutions and large enterprises can effectively analyze and predict the dynamic evolution trend of cross-organizational learning knowledge network. And timely choice of intervention or control means to guide the knowledge network to play a better role in collaborative innovation, knowledge transfer, cross-organizational learning and so on.
【作者單位】: 廣州市社會(huì)科學(xué)院產(chǎn)業(yè)經(jīng)濟(jì)與企業(yè)管理研究所;北京市長城企業(yè)戰(zhàn)略研究所;
【基金】:廣州市社會(huì)科學(xué)院青年課題
【分類號(hào)】:C913.4

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1 陳婷;王麗;;不作為慣性效應(yīng)[J];經(jīng)濟(jì)研究導(dǎo)刊;2010年36期

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本文編號(hào):2314555

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