在线用户评论行为时效特征影响因素实证研究
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摘要
[目的/意义]针对我国在线用户评论习惯,探索用户评论行为对评论时间的影响作用因素,对电子商务运营商探究用户评论行为规律及探索潜在用户评论时间偏好具有重要的潜在商业价值。[方法/过程]基于TAM模型抽取在线用户评论行为时间特征规律研究的影响因素并构建模型,通过抽取消费者购买行为和评论行为的时间间隔为时间序列,通过多元线性回归模型进行假设验证。[结果/结论]通过对在线评论数据的实例验证,本文所构模型能够很好地发现在线用户评论行为对评论时间的影响作用关系,对消费者评论行为的时间特征规律发现和预测具有辅助作用。
[Objective/Significance] In view of the online user review habits in China,exploring the factors affecting the user comment behavior on the comment time,it has important potential commercial value for e-commerce operators to explore the behavior of user comments and explore the potential user comment time preference. [Methods/Process] Based on the TAM model,the influencing factors of the online user comment behavior time characteristic law were extracted and the model was constructed. The time interval was extracted by extracting the time interval between the consumer purchase behavior and the comment behavior,and the hypothesis verification was performed by the multiple linear regression model.[Result/Conclusion] Through the example verification of the online comment data,the model constructed in this paper could well discover the influence of online user comments on the comment time,and have a supporting effect on the time feature law discovery and prediction of consumer comment behavior.
[Objective/Significance] In view of the online user review habits in China,exploring the factors affecting the user comment behavior on the comment time,it has important potential commercial value for e-commerce operators to explore the behavior of user comments and explore the potential user comment time preference. [Methods/Process] Based on the TAM model,the influencing factors of the online user comment behavior time characteristic law were extracted and the model was constructed. The time interval was extracted by extracting the time interval between the consumer purchase behavior and the comment behavior,and the hypothesis verification was performed by the multiple linear regression model.[Result/Conclusion] Through the example verification of the online comment data,the model constructed in this paper could well discover the influence of online user comments on the comment time,and have a supporting effect on the time feature law discovery and prediction of consumer comment behavior.
引文
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[12]尹英姿.基于TAM的酒店在线评论参与行为研究[D].青岛:中国海洋大学,2012.
[13]晏芳.在线评论对消费者行为意向的影响研究[D].北京:中国农业大学,2015.
[14]黄婷婷,曾国荪,熊焕亮.基于商品特征关联度的购物客户评论可信排序方法[J].计算机应用,2014,34(8):2322-2327,2341.
[15]Hu N,Zhang J,Pavlou P A.Overcoming the J-shaped Distribution of Product Reviews[J].Communications of the ACM,2009,52(10):144-147.
[16]张艳丰,李贺,翟倩,等.基于模糊TOPSIS分析的在线评论有用性排序过滤模型研究---以亚马逊手机评论为例[J].图书情报工作,2016,60(13):109-117,125.
[2]Chen Y,Xie J.Online Consumer Review:Word-of-Mouth as a New Element of Marketing Communication Mix[J].Management Science,2008,54(3):477-491.
[3]Zhu F,Zhang X.Impact of Online Consumer Reviews on Sales:The Moderating Role of Product and Consumer Characteristics[J].Journal of Marketing,2010,74(2):133-148.
[4]Jin L,Hu B,He Y.The Recent Versus the Out-dated:An Experimental Examination of the Time-variant Effects of Online Consumer Reviews[J].Journal of Retailing,2014,90(4):552-566.
[5]Tirunillai S,Tellis G J.Mining Marketing Meaning from Online Chatter:Strategic Brand Analysis of Big Data Using Latent Dirichlet Allocation[J].Journal of Marketing Research,2014,51(4):463-479.
[6]龚艳萍,黄凯,张琴,等.新产品预告的时间距离、消费者在线评论及其购买目标的关系研究[J].研究与发展管理,2015,(4):36-44.
[7]汪涛,王魁,陈厚,等.时间间隔何时能够提高在线评论的有用性感知---基于归因理论的视角[J].商业经济与管理,2015,(2):46-56.
[8]胡常春,宁昌会.在线追评何时比初评更有用?---基于时间间隔和产品类型的调节效应分析[J].预测,2017,36(4):36-42.
[9]F D Davis.Perceived Usefulness,Perceived Ease of Use,and User Acceptance of Information Technology[J].MIS Quarterly,1989,13(3):319-340.
[10]Hsu C L,Yu L C,Chang K C.Exploring the Effects of Online Customer Reviews,Regulatory Focus,and Product Type on Purchase Intention:Perceived Justice as a Moderator[J].Computers in Human Behavior,2017,69:335-346.
[11]Lee G,Lin H.Customer Perceptions of E-service Quality in Online Shopping[J].International Journal of Retail&Distribution Management,2005,33(2):161-176.
[12]尹英姿.基于TAM的酒店在线评论参与行为研究[D].青岛:中国海洋大学,2012.
[13]晏芳.在线评论对消费者行为意向的影响研究[D].北京:中国农业大学,2015.
[14]黄婷婷,曾国荪,熊焕亮.基于商品特征关联度的购物客户评论可信排序方法[J].计算机应用,2014,34(8):2322-2327,2341.
[15]Hu N,Zhang J,Pavlou P A.Overcoming the J-shaped Distribution of Product Reviews[J].Communications of the ACM,2009,52(10):144-147.
[16]张艳丰,李贺,翟倩,等.基于模糊TOPSIS分析的在线评论有用性排序过滤模型研究---以亚马逊手机评论为例[J].图书情报工作,2016,60(13):109-117,125.