网络视频实时在线评论用户认可度的影响因素

doi:10.11885/j.issn.1674-5094.2020.12.14.01

摘要/Abstract

摘要： 目前，网络视频实时在线评论激增带来的信息爆炸，使实时在线评论信息管理成为重要议题。以往对实时在线评论的研究多集中于个人行为结果方面，而缺乏对实时在线评论信息管理问题的关注；同时，现有视频网站对实时在线评论信息的管理也存在诸多局限。以爱奇艺视频网站热度榜中的电影为样本，基于信息质量维度框架并采用Tobit模型对实时在线评论用户认可度的影响因素进行研究，结果发现：实时在线评论的新颖性、相似性、情感倾向性和信息丰富性，均对实时在线评论的用户认可度存在显著影响，其中新颖性和相似性影响作用最大。视频网站可以据此进一步强化实时在线评论管理，改善用户的体验，保持用户的粘性。

关键词: 实时在线评论, 用户认可度, Tobit模型, 网络信息技术, 信息质量要素

Abstract: The explosion of real-time online comments makes the management of real-time online comments an important issue at present. Previous studies on real-time online reviews focused more on personal behavior consequences than on information management. On the other hand, there exist limitations in the information management of real-time online comments in video websites. Based on the framework of information quality dimension,this paper uses Tobit model to study the factors influencing user acceptance of real-time online comment. Novelty, similarity, emotional orientation and information richness of real-time online comments have significant effects on user acceptance,among which novelty and similarity play the most important role. The research results can provide some help in the management of real-time online comment of video websites for better viscosity of users.

Key words: real-time online comment, user acceptance, Tobit model, information technology, element of information quality

中图分类号:

袁海霞, 陆亦蘅, 方青青. 网络视频实时在线评论用户认可度的影响因素[J]. 西南石油大学学报(社会科学版), 2021, 23(3): 37-46.

YUAN Haixia, LU Yiheng, FANG Qingqing. Factors Influencing User Acceptance of Real-Time Online Comments[J]. Journal of Southwest Petroleum University(Social Sciences Edition), 2021, 23(3): 37-46.

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参考文献

[1] 龚诗阳,李倩,余承锬. 在线社交对消费者需求的影响研究——基于网络视频产业的实证分析[J]. 中国软科学, 2017(6):39-48.
[2] 王敏,徐健. 视频弹幕与字幕的情感分析与比较研究[J]. 图书情报知识,2019(5):109-119.
[3] 仝冲,赵宇翔. 基于内容分析法的弹幕视频网站用户使用动机和行为研究[J]. 图书馆论坛,2019,39(6):80-89.
[4] Koroleva K, Krasnova H, Oliver Günther. ‘Stop Spamming Me!’-Exploring Information Overload on Facebook[C]//Americas Conference on Information Systems. AMCIS 2010 Proceedings, New York, 2010:447.
[5] 喻昕,许正良. 网络直播平台中弹幕用户信息参与行为研究——基于沉浸理论的视角[J]. 情报科学, 2017,35(10):147-151.
[6] Forman C, Ghose A, Wiesenfeld B. Examining the relationship between reviews and sales:the role of reviewer identity disclosure in electronic markets[J]. Information Systems Research, 2008, 3(19):291-313.
[7] Hu N, Liu L, Zhang J J. Do online reviews affect product sales? The role of reviewer characteristics and temporal effects[J]. Information Technology and Management, 2008, 3(9):201-214.
[8] Weiss A M, Lurie N H, Macinnis D J. Listening to strangers:whose responses are valuable,how valuable are they,and why?[J]. Journal of Marketing Research, 2008, 4(45):425-436.
[9] Racherla P, Friske W. Perceived ‘usefulness’ of online consumer reviews:an exploratory investigation across three services categories[J]. Electronic Commerce Research and Applications, 2012, 6(11):548-559.
[10] Brown J, Broderick A J, Lee N. Word of mouth communication within online communities:conceptualizing the online social network[J]. Journal of Interactive Marketing, 2007,3(21):2-20.
[11] 殷国鹏. 消费者认为怎样的在线评论更有用?——社会性因素的影响效应[J]. 管理世界, 2012(12):115-124.
[12] Park D H, Lee J. eWOM overload and its effect on consumer behavioral intention depending on consumer involvement[J]. Electronic Commerce Research and Applications, 2009, 4(7):386-398.
[13] Doh S J, Hwang J S. How consumers evaluate eWOM (electronic word-of-mouth) messages[J]. CyberPsychology&Behavior, 2009, 4(7):386-398.
[14] Schindler R M, Bickart B. Perceived helpfulness of online consumer reviews:the role of message content and style[J]. Journal of Consumer Behavior, 2012, 3(11):234-243.
[15]
[15] Pan Y, Zhang J Q. Born unequal:a study of the helpfulness of user-generated product reviews[J]. Journal of Retailing, 2011, 4(87):598-612.
[16] Ghose A, Ipeirotis P G. Estimating the helpfulness and economic impact of product reviews:mining text and reviewer characteristics[J]. IEEE Transactions on Knowledge and Data Engineering, 2011, 10(23):1498-1512.
[17] He M, Ge Y, Chen E, et al. Exploring the Emerging Type of Comment for Online Videos:DanMu[J]. ACM Transactions on the Web, 2017,12(1):1-33.
[18] Fang J, Chen L, Wen C, et al. Co-viewing experience in video websites:the effect of social presence on e-loyalty[J]. International Journal of Electronic Commerce, 2018,22(3):446-476.
[19] 黄敏学,王峰,谢亭亭. 口碑传播研究综述及其在网络环境下的研究初探[J]. 管理学报, 2010, 7(1):138-146.
[20] 江晓东. 什么样的产品评论最有用?——在线评论数量特征和文本特征对其有用性的影响研究[J]. 外国经济与管理, 2015,37(4):41-55.
[21] Wang R Y, Strong D M. Beyond Accuracy:What data quality means to data consumers[J]. Journal of Management Information Systems, 1996, 12(4):5-33.
[22] 郝媛媛,叶强,李一军. 基于影评数据的在线评论有用性影响因素研究[J]. 管理科学学报,2010, 13(8):78-88.
[23]
[23] Ba S, Pavlou P A. Evidence of the effect of trust building technology in electronic markets:price premiums and buyer behavior[J]. MIS Quarterly, 2002,3(26):243-268.
[24] Wu P, Heijden H V D, Korfiatis N. The influences of negativity and review quality on the helpfulness of online reviews[C]//International Conference on Information Systems. Shanghai,2011.
[25] Mudambi S M, Schuff D. What makes a helpful online review? A study of customer reviews on Amazon. com[J]. MIS Quarterly, 2010, 1(34):185-200.
[26] 邢美凤,过仕明. 文本内容新颖性探测研究综述[J]. 情报科学, 2011,29(7):1098-1103.
[27] 王平,陈启杰,宋思根. 网络互助社群中消费者内容选择的影响因素研究——以IT产品消费为例[J]. 财贸经济, 2011(4):117-124.
[28] 苏正. 微信用户获取信息质量的满意度调查分析.[D]. 郑州:郑州大学, 2017.
[29] Merriam-Webster.Novelty[EB/OL].[2020-05-01].https://www.merriam-webster.com/dictionary/novelty.
[30] Berger J A, Milkman K L. What Makes Online Content Viral?[J]. Journal of Marketing Research, 2009, 49(8):192-205.
[31] Sen S, Lerman D. Why are you telling me this? An examination into negative consumer reviews on the web[J]. Journal of Interactive Marketing, 2007, 4(21):76-94.
[32] 游浚,张晓瑜,杨丰瑞. 在线评论有用性的影响因素研究——基于商品类型的调节效应[J]. 软科学, 2019, 33(5):140-144.
[33] 孙竹梅,汪志兵. 基于信息特征的微博健康信息采纳研究[J]. 情报理论与实践,2019,42(3):146-152.
[34] 徐翔. 中国文化在视频自媒体的传播效果及其影响因素分析——基于YouTube的样本挖掘与实证研究[J]. 北京邮电大学学报(社会科学版),2016,18(5):1-7.
[35] 沈阳. 一种基于关键词的创新度评价方法[J]. 情报理论与实践, 2007(1):125-127.
[36] Zhao L, Zhang M, Ma S. The nature of novelty detection[J]. Information Retrieval, 2006, 5(9):521-541.
[37] Allan J, Wade C, Bolivar A. Retrieval and novelty detection at the sentence level[C]//International Conference on Information Systems, Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, New York,2003:314-321.
[38] Zhang Q, Wang W, Chen Y. Frontiers:In-Consumption Social Listening with Moment-to-Moment Unstructured Data:The Case of Movie Appreciation and Live Comments[J]. Marketing science, 2020,39(2):285-295.
[39] Lavie T, Oron-Gilad T, Meyer J. Aesthetics and Usability of in-vehicle Navigation Displays[J]. International Journal of Human-Computer Studies, 2011, 1(69):80-99.
[40] 周华林,李雪松. Tobit模型估计方法与应用[J]. 经济学动态, 2012(5):105-119.