題目：Online Review Volume, Customer Agility and Product Performance: An Empirical Big Data Study in the Mobile App Industry
主講人：樊衛(wèi)國(guó) 教授 （弗吉尼亞理工大學(xué)）
時(shí)間：2017年6月22日（周四）上午10：00
地點(diǎn)：主樓418
主講人簡(jiǎn)介：
任Management Science(MS)，Management Information System Quarterly (MISQ)，IEEE Transactions on Evolutionary Computation (TEC)，IEEE Transactions on Knowledge and Data Engineering (TKDE)，ACM Transactions on Information Systems (TOIS)等期刊審稿人。任IEEE Technical Committee on Digital Libraries成員，任MISQ, Information and Management, Journal of Database Management編委會(huì)成員, 任近20個(gè)國(guó)際會(huì)議評(píng)審委員。
近年來，主講人一直在美國(guó)弗吉尼亞理工大學(xué)致力于社會(huì)計(jì)算、大數(shù)據(jù)及文本挖掘在商業(yè)領(lǐng)域的應(yīng)用，商務(wù)智能與大數(shù)據(jù)的研究與開發(fā), 社交媒體數(shù)據(jù)分析及用戶行為， 智慧健康等問題的研究, 并取得了豐碩且具有創(chuàng)新性和影響力的成果。其研究成果已應(yīng)用到金融，營(yíng)銷，互聯(lián)網(wǎng)金融，眾包，運(yùn)營(yíng)管理，互聯(lián)網(wǎng)技術(shù)，信息管理,智慧健康等重要領(lǐng)域。
主要的成果：（1）將文本挖掘與分析技術(shù)應(yīng)用到社交媒體與用戶產(chǎn)生的內(nèi)容進(jìn)行產(chǎn)品缺陷的識(shí)別與質(zhì)量監(jiān)控。該研究被美國(guó)紐約時(shí)報(bào)強(qiáng)力報(bào)道。現(xiàn)正在商業(yè)化。(2）首次將文本挖掘技術(shù)應(yīng)用到美國(guó)上市公司財(cái)務(wù)報(bào)表欺詐舞弊風(fēng)險(xiǎn)的預(yù)測(cè)。該技術(shù)有非常高的商業(yè)前景，已經(jīng)商業(yè)化。（3）全球第一個(gè)致力于社交網(wǎng)絡(luò)服務(wù)公司戰(zhàn)略競(jìng)爭(zhēng)行為的實(shí)證研究。（4）首次在信息管理領(lǐng)域利用文本挖掘技術(shù)自動(dòng)對(duì)網(wǎng)上論壇用戶的討論有用性進(jìn)行打分。(5) 較早研究用戶在網(wǎng)上知識(shí)社區(qū)里的信息共享行為及信息傳播特征。(6) 全球首次將遺傳規(guī)劃應(yīng)用到搜索引擎排序函數(shù)的優(yōu)化， 并成功將該技術(shù)拓展應(yīng)用到圖像檢索與查找領(lǐng)域. (7) 首次提出研究學(xué)者合作能力指數(shù)C-index, 能準(zhǔn)確的對(duì)學(xué)者的合作能力進(jìn)行測(cè)量, 定位。（8）利用深度學(xué)習(xí)技術(shù)做癌癥檢測(cè)智能診斷系統(tǒng)。腦癌檢測(cè)已達(dá)到國(guó)際先進(jìn)水平。
發(fā)表論文170 余篇. 其中有國(guó)際影響力的期刊論文60 余篇。近五年谷歌學(xué)者引用次數(shù)超過2108 次. H-index = 39。全球谷歌學(xué)者引用排名商務(wù)數(shù)據(jù)分析第14, 商務(wù)智能排名第 8, 文本挖掘排名第44, 社會(huì)計(jì)算第50。
報(bào)告摘要：
This study examines product development based on online customer reviews. We develop a tension perspective to reconcile contradictories in prior literature and investigate relationships among review volume, product performance, and customer agility, which describes the effectiveness of a developer’s response to customers’ demands. We argue that because demands embedded in large-volume reviews are valuable but difficult to respond, review volume has a curvilinear relationship with customer agility. This relationship is moderated by the developer’s number of sibling products and the variance of product ratings. Furthermore, because making effective responses to demands simultaneously raises customers’ willingness to purchase and product development costs, customer agility has a curvilinear relationship with product performance. We test our model using a large mobile app dataset consisted of three million online reviews and find support for our hypotheses. Our findings of the curvilinear relationships and moderators provide nuanced explanations on how online reviews facilitate product development.
（承辦：管理工程系，科研與學(xué)術(shù)交流中心）
