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线上消费者发现“惊喜”的产品搜索行为研究 被引量:7

A Study of Online Consumers' Serendipitous Product Search Behavior
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摘要 线上消费者的产品搜索是网络购物过程中极为重要的一环,但前人研究多集中在如何帮助消费者找到与他们过去的购物行为或喜好一致或相关的产品。然而,在搜索过程中发现令人"惊喜"的好产品其实是一个非常有趣、普遍而且重要的方面。本研究旨在探索电商平台提供的多个主要搜索功能(即个性化推荐功能、非个性化浏览功能、基于用户回忆的关键词搜索功能、基于用户识别的分类搜索功能)对消费者发现"惊喜"的程度和搜索效率的影响,以及发现惊喜和搜索效率对消费者决策满意度的影响。我们通过实验调查法收集到了135条有效数据,并用PLS模型对数据进行分析。结果表明,电商平台非个性化的推荐有助于消费者发现惊喜,而基于回忆的关键词搜索功能对发现惊喜有反向作用。基于识别的分类搜索能有效提升搜索效率,而基于回忆的关键词搜索功能会削弱个性化浏览功能对提高用户搜索效率的正向作用。用户发现惊喜的程度和搜索效率都会显著影响其购物决策满意度。最后,我们阐述了此次研究的重要理论意义和实践启示,也提出了一些不足与未来的研究方向。 Online consumers' product search is essential to their shopping experience and decision making.Existing research mainly focuses on how to improve the accuracy of product recommendation or search systems. Yet,evidence suggests that consumers may not always want to search for information by relying on preset preferences or keywords-either because they may not be clear about what they really need at the beginning of a search or because their search goals may change during a search process. This study explores how product search features direct users ' adaptive search and focuses on two aspects of users' search experience, perceived serendipity and search efficiency. The effect of search serendipity and efficiency on users' decision satisfaction is also examined. Results from a survey experiment show that non-personalized recommendations can improve search serendipity, whereas recall-based keyword search has a negative effect on serendipity. Recognition-based category search improves search efficiency, whereas recall-based keyword search and personalized recommendations dilute each other's effect on efficiency. Search serendipity and efficiency both contribute to users' decision satisfaction. Theoretical and practice implications are discussed.
作者 易成 周姗姗
出处 《中国管理科学》 CSSCI 北大核心 2016年第S1期329-336,共8页 Chinese Journal of Management Science
基金 国家自然科学青年科学基金资助项目(71402079) 清华大学自主科研青年教师基础研究专项(20151080393)
关键词 惊喜 搜索效率 搜索功能 信息搜索行为 决策满意度 serendipity search efficiency search features information search behavior decision satisfaction
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  • 1王天夫.社会研究中的因果分析[J].社会学研究,2006(4):132-156. 被引量:79
  • 2张光明,龚雯莉.从格式塔心理学看平面设计[J].科技信息,2007(15):128-128. 被引量:2
  • 3Abbattista F. , Degemmis M. , Fanizzi N. , et al. Learn ing User Profiles for Content - Based Filtering in e - Commerce [R]. SSRN.
  • 4Balabanovic M. and Shoham Y. , Fab: Content - Based Collaborative Recommendation. Communication of the ACM [J]. 1997,40(3):66 -72.
  • 5Bo Xiao. Izak Benbasat. E -Commerce Product Recommendation Agents: Use, Characteristics, and Impact[J]. MIS Quarterly, 2007,31 ( 1 ) :137 -209.
  • 6Breese, J. S. , Heckerman, D. , & Kadie, C. Empirical Analysis of Predictive Algorithms for Collaborative Filtering [ C]. Proceedings of the 14th Conference on Uncertainly in Artificial Intelligence, 1998. 43 C52.
  • 7Byron L.D. Bezerra, Francisco de A.T. de Carvalho. A Symbolic Approach for Content - based Information Filtering [ J ]. Information Processing Letters,2004, (92) :45 - 52.
  • 8CHEN Jian, YIN Jian, HUANG Jin. Automatic Content - Based Recommendation in e -Commerce [ C ]. 2005 Ieee International Conference on e - Technology, e - Commerce and e - Service ( EEE' 05 ) ,2005. 748 - 753.
  • 9D. Billsus and M. J. Pazzani. Learning Collaborative Information Filters. Proceedings of the Fifteenth International Conference on Machine Learning[ C]. Madison, WI, 1998. Morgan Kaufman.
  • 10Dan Ariely. John G. Lynch, Jr. Manuel Aparicio IV. Learning by Collaborative and Individual- Based Recommendation Agents [ R ]. SSRN, 2007.

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