[1] Weathers D, Swain S D, Grover V. Can online product reviews be more helpful? Examining characteristics of information content by product type[J]. Decision Support Systems, 2015, 79:12-23 [2] Yin Dezhi, Bond S, Zhang Han. Anxious or angry? Effects of discrete emotions on the perceived helpfulness of online reviews[J]. MIS Quarterly, 2013,38(2):539-60. [3] Archak N, Ghose A, Ipeirotis P G. Deriving the pricing power of product features by mining consumer reviews[J]. Management Science, 2011, 57(8):1485-1509. [4] Chevalier J A,Mayzlin D. The effect of word of mouth on sales:Online book reviews[J]. Journal of Marketing Research, 2006, 43(3):345-354. [5] Decker R,Trusov M. Estimating aggregate consumer preferences from online product reviews[J]. International Journal of Research in Marketing, 2010, 27(4):293-307. [6] Qi Jiayin, Zhang Zhenping,Jeon S, et al. Mining customer requirements from online reviews:A product improvement perspective[J]. Information & Management, 2016, 53(8):951-963. [7] Xiao Shengsheng, Wei Chih-Ping, Dong Ming. Crowd intelligence:Analyzing online product reviews for preference measurement[J]. Information & Management, 2016, 53(2):169-182. [8] Li Yung-Ming, Chen Hsuan-Ming, LiouJyh-Hwa, et al. Creating social intelligence for product portfolio design[J]. Decision Support Systems, 2014, 66:123-134. [9] Lycett M. ‘Datafication’:making sense of (big) data in a complex world[J]. European Journal of Information Systems, 2013, 22(4):381-86. [10] Chapman J. Design for (emotional)durability[J]. Design Issues, 2009, 25(4):29-35. [11] Guiltinan J. Creative destruction and destructive creations:Environmental ethics and planned obsolescence[J]. Journal of Business Ethics, 2009, 89(1):19-28. [12] Northrop E. Prosperity Without Growth:Economics for a FinitePlanet[J]. Eastern Economic Journal, 2014, 40(3):440-442. [13] 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, 19(3):291-313. [14] 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. [15] Ngo-Ye T L, Sinha A P. The influence of reviewer engagement characteristics on online review helpfulness:A text regression model[J]. Decision Support Systems, 2014, 61:47-58. [16] 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. [17] Liu Yang, Huang Xiangji, An Aijun, et al. Modeling and predicting the helpfulness of online reviews[C]//Proceedings of 2008 Eighth IEEE International Conference on Data Mining, Pisa, Italy, December 15-19, IEEE,2008:443-452. [18] 刘伟,徐鹏涛. O2O电商平台在线点评有用性影响因素的识别研究——以餐饮行业O2O模式为例[J]. 中国管理科学,2016, 24(5):168-176. [19] Liu Ying, Jin Jian, Ji Ping, et al. Identifying helpful online reviews:A product designer's perspective[J]. Computer-Aided Design, 2013, 45(2):180-194. [20] Fayyad U, Piatetsky-Shapiro G, Smyth P. The KDD process for extracting useful knowledge from volumes of data[J]. Communications of the ACM, 1996, 39(11):27-34. [21] 胡浩,祁国宁,方水良,等.基于产品服务数据的客户需求挖掘[J].浙江大学学报(工学版),2009, 43(3):540-545. [22] 王君珺,闫强.不同热度搜索型产品的在线评论对销量影响的实证研究[J].中国管理科学,2013,21(S2):406-411. [23] 涂海丽,唐晓波,谢力.基于在线评论的用户需求挖掘模型研究[J].情报学报, 2015, 34(10):1088-1097. [24] 石文华,钟碧园,张绮.在线影评和在线短评对票房收入影响的比较研究[J].中国管理科学, 2017, 25(10):162-170. [25] Zhou Feng, Jiao Jianxin, Linsey J S. Latent customer needs elicitation by use case analogical reasoning from sentiment analysis of online product reviews[J]. Journal of Mechanical Design, 2015, 137(7):071401. [26] Selden L, MacMillan I C. Manage customer-centric innovation-systematically[J]. Harvard Business Review, 2006, 84(4):108. [27] Tucker C S, Kim H M.Data-driven decision tree classification for product portfolio design optimization[J]. Journal of Computing and Information Science in Engineering, 2009, 9(4):041004. [28] Mela C F, Gupta S, Lehmann D R. The long-term impact of promotion and advertising on consumer brand choice[J]. Journal of Marketing Research, 1997:248-261. [29] Böttcher M, Spott M, Kruse R. Predicting future decision trees from evolving data[C]//Proceedings of 2008 Eighth IEEE International Conference on Data Mining, Pisa, Italy, December 15-19,IEEE, 2008:33-42. [30] Tucker C S, Kim H M. Trending mining for predictive product design[C]//Proceedings of ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Montreal, Canada, August 15-18, American Society of Mechanical Engineers, 2010:1007-1021. [31] 陆佳圆,冯毅雄,谭建荣,等.产品顾客需求权重的动态趋势预测与分析[J].计算机集成制造系统,2011,17(10):2129-2136. [32] Von Ahn L, Dabbish L. Labeling images with a computer game[C]//Proceedings of the SIGCHI conference on Human factors in computing systems, Vienna, Austria, April 24-29,Association for Computing Machinery, 2004:319-326. [33] Hu Minqing, Liu Bing. Mining and summarizing customer reviews[C]//Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining,Seattle, USA, August 22-25, Association for Computing Machinery, 2004:168-177. [34] Liu Jian, Wu Gengfeng, Yao Jianxin. Opinion searching in multi-product reviews[C]//Proceedings of the Sixth IEEE International Conference on Computer and Information Technology, Seoul, Korea,September 20-22, IEEE, 2006:25. [35] Gibbons J D, Kendall M. Rank correlation methods[M]. New York:Oxford University Press, 1990. [36] Yue S, Pilon P, Cavadias G. Power of the Mann-Kendall and Spearman's rho tests for detecting monotonic trends in hydrological series[J]. Journal of Hydrology, 2002, 259(1-4):254-271. |