摘要
随着网络技术和信息技术的发展,我国电子商务呈现出迅猛的发展势头。消费者在线购物的蓬勃发展,给电子商务企业带来了前所未有的发展机遇。电子商务的飞速发展为消费者提供了庞大的商品选择和海量的商品信息,同时也为消费者带来了巨大的认知负荷。为此,近年来电子商务网站普遍推出了各种推荐服务,比如协同推荐、热点商品推荐、商品分类组合选择等等,以此减少消费者在商品海洋中决策的盲目性。
但就目前来看,消费者在商品选择决策中缺乏商品相关知识支持这一问题普遍存在,尤其是价位较高的高科技商品,由于直接与购买利益相关,消费者在决策中可能更多会体现出一种理性决策意愿,因此他们应该会希望网站能够提供与商品购买知识相关的推荐服务,以此提高购物决策效率。
在此背景下,本论文提出了在线知识推荐服务的构想,即在原来以商品推荐为主的服务内容基础上,我们认为是否还可以为消费者提供辅助决策的商品相关知识?比如基于效用视角的商品参数简介,甚至可以根据消费者的偏好有选择有重点的推荐。进一步是否还可以提供对商品参数的分析加工知识?比如某价位商品的性价比分析知识等,以此真正减轻消费者购物决策中的认知负荷。
为此,本论文需要对这种知识推荐服务构想的实施提供理论依据,即本论文需要依据相关购物决策、认知等理论以及实验分析方法对消费者在线购物决策机理以及知识在其中的作用机制进行分析,以为知识推荐服务内容的精准设计提供依据。本论文还需要分析消费者如何才能更有效地接受未来的知识推荐服务,以为知识推荐服务整体框架设计提供理论依据。
在全面梳理国内外相关研究的基础上(见论文第2章),本论文主要对以下的科学问题进行了研究：
(1)消费者在线商品选择决策机理是什么?其中商品相关知识引发消费者商品选择决策的内在机制分析是重点,具体包括知识支持主要发生在消费者商品选择决策过程中的哪些环节?支持机理是什么?在此基础上还需要对消费者接受、学习商品相关知识推荐的可能意愿进行分析,具体包括是否每个消费者都会产生这种基于知识支持进行商品认知的购买决策需求?与消费者的知识背景、性别、认知需求是否有关联?由此解决是否需要构建知识推荐服务的问题。
这部分研究也是本论文的创新点所在,就目前文献来看,有关商品购买决策中的知识支持研究,主要集中在品牌知识对消费者购买决策行为的影响研究上,具体包括品牌知名度、形象等如何影响消费者对品牌的信任和满意度,以及如何影响消费者与品牌的关系,而在商品选择决策环节中的知识支持机理研究尚未见到。
针对以上研究问题,本论文具体进行了以下工作：
①基于消费者决策行为理论、购物决策理论等相关理论对消费者在线商品选择决策过程进行了分析,构建了消费者在线商品选择决策模型；
②基于信息加工理论、需求—动机—行为理论等相关理论对消费者在线商品选择决策中的知识支持机理、消费者接受与学习商品相关知识意愿及原因进行了深入分析；
③基于问卷调研方式,对消费者在线商品选择决策中的知识需求及接受与学习商品相关知识意愿进行了实证分析。
通过理论与实证分析,本论文得到的主要结论是：
①消费者普遍需要商品相关知识以便辅助其对商品作出选择,尤其是高科技商品,因此高科技商品是知识推荐服务的重点；
②消费者普遍愿意接受和学习来自外部的商品相关知识,包括在线推荐服务,以便辅助其对商品作出选择,由此说明知识推荐服务将会受到欢迎；
③消费者大多愿意对商品参数进行认知,以便更好地对商品作出选择,由此说明绝大部分消费者并不排斥认知负荷高的商品相关知识推荐,因此商品参数的通俗解释可以成为知识推荐服务的基本内容；
④不同认知需求、性别的消费者在对不同加工级别的知识推荐内容选择上存在差异,因此向不同特征的消费者提供不同加工级别的知识推荐是非常有必要的。
具体见论文第3章。
(2)如何通过控制实验方法观测消费者在模拟知识推荐情境下的商品知识接受状态、决策行为偏好?具体包括商品参数知识模拟情境设计；消费者知识接受状态观测指标设计,以及在此知识接受状态下决策行为偏好观测指标设计；如何通过控制实验方法观测个体因素对消费者知识接受状态及决策行为偏好的影响?重点观测个体知识背景、性别以及认知需求的影响。本论文将首次尝试采用心理测量方法对实验被试认知需求水平进行测定,由此为知识推荐服务内容的精准设计提供重要依据。
针对以上研究问题,本论文具体进行了以下工作：
①对数码相机商品选择决策实验的模拟平台进行了设计和开发,以避免现场实验带来的各种干扰,这也是本论文的创新点之一,以往研究中还未见到通过模拟实际电子商务网站商品购买环境去提炼需要观测分析的行为要素的实验方法,具体包括实验界面设计、参数选择、参数解释、性价比分析得分、用户评论、后台记录消费者决策行为等,由此满足消费者商品选择决策行为观测分析需求；
②依据心理测量方法对实验被试进行了认知需求(即喜欢思考)水平测量,并进行了差异分析,为今后相关研究的深化迈出了第一步；
③利用专家知识重要度及参数支持度设计了与专家知识接近程度的知识接近度指标,由此来观测被试决策中的知识接受状态,即知识接近度值越小,消费者离专家知识越远,知识需求越大；
④利用参数偏好度设计了参数类型偏好值指标,由此来观测在模拟知识推荐情境下的被试商品参数类型偏好,即偏好值越大,消费者越偏好对此类型的参数进行认知；
⑤利用关联规则挖掘方法设计了参数认知路径偏好分析方案,由此来观测模拟知识推荐情境下的被试商品参数认知规则,即在选择一个参数之后,与浏览下一个参数是否存在前后的关联关系。
上述指标的设计都在其他研究基础上结合本论文研究特点作了一定的创新。
通过实证分析,本论文得到的主要结论是：
①就数码相机商品而言,消费者整体对商品相关知识处于匮乏状态,需要知识推荐；
②不同性别、知识背景的消费者在参数类型偏好上存在差异,如女性消费者偏好魅力参数等,而不同认知需求水平的消费者在参数类型偏好上并无差异；
③不同特征的消费者在参数认知路径偏好上存在一定的差异,其中认知需求高的消费者的参数认知路径集中于性能参数及功能参数。
以上研究结论为知识推荐服务内容设计提供了启示,具体见论文第4、5、7章。
(3)最后,本论文对基于购物决策需求的推荐服务接受影响因素进行了分析,由此全面了解构建有效的知识推荐服务主要受哪些因素影响,为构建知识推荐服务整体框架设计提供建议,从而更好地辅助消费者在线购物决策过程。
本论文具体进行了以下工作：
①基于技术接受理论,以“亚马逊中国”网站推荐服务为例,构建了消费者有效接受推荐服务影响因素模型；
②对该模型进行了问卷调研,以便收集用户对对“亚马逊中国”网站推荐服务使用的感知数据；
③基于实验数据,采用结构方程模型方法对模型进行了实证分析,验证提出的假设。
本论文得到的有用性、易用性的分析结论为知识推荐服务整体框架设计提供了依据。具体见论文第6、7章。
With the development of Internet and information technologies, e-commerce in our country is increasingly developing, and consumers' online shopping is booming, which offers e-commerce businesses unprecedented opportunities. While the rapid development of e-commerce provides consumers a huge selection of commodity options and information, it also brings about enormous cognitive load. That's why many websites put forward all kinds of recommendation services, such as collaborative recommendation, recommended hot commodity, commodity classification of portfolio selection, and so on, in order to reduce the blindness of the consumer decision-making in the ocean of goods.
But so far, it is a widespread issue that consumers are quite in lack of commodity-related knowledge, especially for the higher price of high-tech product selection process. Because of the direct interests of purchase, consumers may show a more rational decision-making intention, so they should hope that the website can provide commodity purchasing knowledge of relevant recommendation service, so as to improve the decision-making efficiency.
Under this background, this paper puts forward the idea of online recommendation service of knowledge, namely on the bases of the original commodity recommendation, whether we can also provide consumers with aided knowledge about commodity in order to make a decision? For example, we can provide with commodity parameter profile which on the utility perspective, what more, we can make recommendations according to the preference of consumer. Further still we can provide commodity parameter knowledge which has been analyzed and processed. For example, the Cost-effective of commodity knowledge, so as to reduce the cognitive load in consumer decision making.
Therefore, this paper provide academic basis for this kind of knowledge recommendation service conception, namely in this paper we will analysis the online consumer shopping decision-making mechanism as well as knowledge in the role of analyzing the mechanism, according to the relevant decision-making theory, cognitive theory and experimental analysis, that provide the basis for accurate design of the knowledge of recommended services, we also need to analyze how consumers can be more effective to accept future recommendation service of knowledge, so to provide a theoretical basis for the overall framework design of recommendation service of knowledge.
Based on a comprehensive review of relevant research at home and abroad (see the second chapter), this paper focuses on the following scientific issues:
(1)What is the mechanism of online decision-making? Among them, the analysis of the internal mechanism of commodity knowledge caused consumer merchandise selection decision is the key, specific knowledge to support select what part of the decision-making in the consumer goods? What is the supporting mechanism? On this basis, the possible intention and reason of accepting and studying recommendation service of knowledge were analyzed, including whether every consumer will need kind of this kind of recommendation service of knowledge. And whether the consumer knowledge background, gender, cognitive demand are related? The resultant solution is needed to construct the recommendation service of knowledge.
This part is also the creative point of this paper. the current literature, the commodity purchase decision support in knowledge study, is mainly concentrated in the influence of brand knowledge on consumers' purchase decision behavior, including how the brand awareness, brand image to influence consumers' trust and satisfaction on brand, then influence the relationship of the consumer and the brand. However mechanism of support in the selection of products in the decision-making aspects of knowledge has yet to be seen.
In view of the above problems, this paper carried out the following work:
①Based on the consumer decision-making behavior theory, decision-making theory and other related theories, we analyze the decision-making process of consumer online selection, and build the decision model for the decision-making process of consumer online selection;
②Based on the information processing theory, demand-motivation-behavior theory and other related theories, we analyze the mechanism of knowledge support, the acceptance and learning of the commodity-related knowledge will and the reasons in the decision-making process of consumer online selection;
③Based on questionnaire investigation, we conduct an empirical analysis the knowledge demand, acceptance and learning product-related knowledge in the decision-making process of consumer online selection.
Through theoretical and empirical analysis, the main conclusions of this paper are:
①Consumers generally require knowledge of the commodity for auxiliary on value choices, especially for high-tech commodities, so the high-tech commodities are the focus of recommendation service of knowledge;
②Consumers are generally willing to accept and learn from external knowledge of related product, including online recommendation service, in order to assist in the choice of commodities, so recommendation service of knowledge will be welcome;
③Most of consumers are willing to learn commodity parameters, in order to make a better choice, so that the vast majority of consumers do not exclude recommendation service of knowledge for commodities which load high cognitive, so the popular explanation of commodity parameter can become the basic content of recommendation service of knowledge k;
④The consumers of different cognitive needs and sex have the differences in the choose of different levels of recommendation service of knowledge, therefore it is very necessary to provide different levels of recommendation service of knowledge to different characteristics of consumers.
See the third chapter of the thesis.
(2)How to observe the knowledge demand and decision behavior preference of consumers online selection decision through controlled experimental? Including designing the observation index of knowledge demand and decision making behavior preference in the commodity selection decision; how to observe the effects of individual factors on consumers' knowledge demand and decision behavior preference through control experimental observation? The key observations are the effects of individual knowledge background, gender and cognitive demand. This paper will be the first attempt to use psychological measurement method on experimental subjects cognitive demand levels, and thus provide an important basis for the precise design of the knowledge of recommended services.
In view of the above problems, this paper carried out the following work:
①We design and develop experiment simulation platform for digital camera selection decision, in order to avoid the field experiment to bring all sorts of interference, which is also the innovation of this paper, previous research has not yet simulated actual electronic commerce website shopping environment in order to refine the observation and analysis of behavioral elements, specific including experimental interface design, parameter selection, parameter interpretation, cost-effective analysis of scoring, user comments, backstage record consumer decision-making behavior, in order to satisfy the demand of observation and analysis of consumer choice behavior;
②We test cognitive demands levels (i.e., like thinking) on the basis of mental measurement experiment, and analysis the difference, a first step in the deepening of research in the future;
③We use preference model and expert knowledge to design the index of degree expert knowledge, in order to observe the demand of subjects decision knowledge, namely the index is more small, the consumer is more far from the expert knowledge, the greater of the need of knowledge;
④we design index of type preference values of weight by using parameters of support degree and order of parameters, in order to observe the commodity parameter type preferences, namely preference value is greater, more consumers prefer this type of parameters;
⑤Using the association rules mining method to design parameters of the cognitive path preference analysis program, in order to observe the commodity parameter cognitive rules. That is after choosing a parameter, whether there is some relationship with visiting the next parameter.
The index of the design in the other model based on the combination of the research and gives some innovative features.
Through empirical analysis, the main conclusions of this paper are:
①for digital cameras goods, The overall consumer are lack of knowledge of the goods;
②Different gender, background consumers have significant difference in the parameter type preferences, such as female consumers prefer the charm of parameters,while consumers of different cognitive levels of demand have no difference in the parameter type;
③Consumers of different characteristics have some differences in the preferences of the parameters of cognitive path, among these consumers the parameters of cognitive path of consumers with high cognitive demand focused on the performance parameters and functional parameters;
The above conclusions provide inspiration for the knowledge of recommended services design, See in particular4,5,7Chapter of the paper.
(3) Finally, we analyze factors which influent the acceptance of recommendation service in the decision-making process, and get a comprehensive understanding the factors which influence the construction of the knowledge recommendation service, and provide advice for the design of the overall framework of the construction, so as to better assist consumers in the decision-making process of online shopping.
This paper carried out the following work:
①Based on the technology acceptance theory, take recommendation service of Amazon China as an example, we build model of factors which influence the efficient acceptance of the recommendation service;
②we undertook a questionnaire survey of the model, in order to collect the using perceptual data about recommendation service of Amazon China;
③Based on the experimental data, we conduct an empirical analysis of the model using structural equation model approach, and verified the hypothesis put forward.
The analysis conclusions of usefulness and ease of use provide a basis for the knowledge of recommended service framework design, see in particular6,7Chapter of the paper.
引文
[1]吕廷洁,陈霞,胡桃.电子商务教程[M].北京：电子工业出版社,2011
[2]中国互联网络信息中心.中国互联网络发展状况统计报告[R].北京：中国互联网网络信息中心,2012
[3]艾瑞咨询集团.中国电子商务行业发展报告[R].北京：艾瑞咨询集团,2012
[4]艾瑞咨询集团.中国网络购物行业年度监测报告[R].北京：艾瑞咨询集团,2012
[5]Zhang Y,Callan J,Minka T.Novelty and Redundancy Detection in Adaptive Filtering[C]. In:Proc of the 25th ACM SIGIR,2002,81-88
[6]Y.Koren.Collaborative Filtering with Temporal Dynamics[C].In:Proc the 15th ACM SIGKDD international conference on Knowledge discovery and data mining,2009, 447-456
[7]Yoshii K.,Goto M..Komatani K.,et al. An Efficient Hybrid Music Recommender System Using an Incrementally Trainable Probabilistic Generative Model [J]. IEEE Transactions on Audio Speech and Language Processing,2008,16(2):435-447
[8]朱妹,程毅.消费者行为学[M].上海,华东理工大学出版社,2009
[9]D.Gefen. E-commerce:the Role of Familiaty and Trust[J]. Omega: The International Journal of Management Science,2000,28(6):725-737
[10]S.Soyeon,A.Mary. An Online Prepurchase Intentions Model:The Role of Intention to Search[J]. Journal of Retailing,2001,77:397-416
[ll]Li Na,Zhang Ping.Consumer online shopping attitudes and behavion:An assessment of research[J].Eighth Americas Conference on Information Systems,2002.508-518
[12]黎志成,刘枚莲.网上购物环境下的消费者行为研究[J].中国管理科学,2002,10(6)：89-91
[13]庞川.陈忠民.罗瑞文.消费者网络信任影响因素的实证分析[J].系统工程理论方法应用,2004(4)：295-299
[14]Arnold,M.J.&Reynolds,K.E. Hedonic shopping motivations [J]. Journal of retailing, 2003,79(2),77-95
[15]Wang,C.C.&Yang,H.W. Passion for online shopping:The influence of personality and compulsive buying[J]. Social Behavior and Personality:An International Journal,2008,36(5),693-706
[16]Rodgers Shelly,Harris Mary Ann. Gender and e-commerce:An exploratory study[J]. Journal of Advertising Research,2003,43,(3):322-329
[17]Korgaokar Pradeep K.,Lori D.Wolin A Multivariate Analysis of web Usage[J]. Journal of Advertising Reseach,1999,3(4):53-68
[18]Bellman S, Lohse G, Johnson E. Predictors of online buying behavior[J]. Communications of the ACM,1999,42(12):32-38
[19]Goldsmith,Ronald E. Explaining and predicting consumer internet to purchase over the internetan exploratory study[J]. Journal of Marketing Theory and Practice,2002, 10,(2):22-28
[20]Wei-Na Lee.The effect of information overload on consumer choice quality in an on-line environment[J]. Psychology & Marketing,2004,21,(3):159-183
[21]Hamburger,Y.A.,&Fine A.The effects of need for cognition on Internet use[J]. Computers in Human Behavior,2007,23(1):880-891
[22]T.L.Childersw.Hednic and Utilitariam motivations for online retail shopping behavior[J]. Journal of Retailing,2001,(77):511-535
[23]Tow Cheung,Michael. Internet-based e-shopping and consumer attitudes:an empirical study[J].Information&Management,2001,38,(5):299
[24]Bhatnagar Amit,Misra Sanjog,Rao H.Raghav.On risk,convenience,and internet shopping behavior[J].Communications of the ACM,2000,43,(11):98-105
[25]Sirkka L.Jarvenpaa,Noam Ttactinsky and Lauri Saarinen.Consumer trust in an internet store:Across-cultural validation[J]. Journal of Computer Mediated Communication, 1999,5,(2):411-435
[26]Pavlou PA. Consumer aeceptance of electronic commerce:integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 2003:7(3):101-34
[27]Heijden H V,Verhagen T,Creemers M.Predicting Online Purchase Behavior:Replications and Tests of Competing Models[C]. Proceedings of the 34th Hawaii International Conference on System Sciences,2001
[28]M.K.O.Lee, E.Turban. A trust Model for Consumer Internet Shopping[J]. International Journal of Electronic Commerce,2001,6(1):75-91
[29]Z.Liao and M.Cheung. Internet-based e-shopping and consumer attitudes:An empirical study[J]. Information & Management,2001,38, (5):299-306
[30]伍丽君.网上消费者行为分析[J].湖北社会科学,2001,(12)：19-20
[31]Engel, J.F.,Blackwell, R.D.Consumer Behavior[M].Chicago:Dryden Press,1982
[32]Aaker,J.L. Dimension of brand personality[J]. Journal of Marketing Research, 1997,34(3):347-356
[33]Kevin lane Keller. Brand Synthesis:The Multidimensionality of Brand Knowledge[J]. Journal of Consumer Research,2003,29(4):595-600
[34]廖成林,柳茂森.关于消费者品牌知识对重复购买行为的影响[J].商业时代,2011,(19)：26-27
[35]杨伟文,刘新.品牌认知对消费者购买行为的影响[J].商业研究,2010,(395)：158-164
[36]雷榕,倪文娟.健身俱乐部消费者信息需求研究——购买经验及产品知识视角[J].首都师范大学学报(自然科学版),2012,33(1)：80-86
[37]马向阳,徐富明,吴修良等.行为决策中的忽略偏差：心理机制与影响因素[J].心理科学进展,,2011,19,(12)：1834-1841
[38]王晓明,李雯,周爱保.认知信息的详尽度与锚定对决策策略的影响[J].心理科学,2011,,3(1)：172-176
[39]秦凤洁.绿色消费态度和绿色消费知识对绿色产品购买行为的影响浅析[J].市场营销,,2011,(1)：41
[40]Slovic P. The construction of preference [J]. American Psychologist,1995,50:364-371
[41]Von Neumann,J. and O.Morgenstern. Theory of Games and Economic Behavior [M]. Princeton University Press,1944
[42]崔兆鸣.对偏好反转现象的解释[J].经济科学,2002,2：122-128
[43]Kahneman D,Tversky A.Prospect theory:An analysis of decisions under risk[J]. Econometrica,1979,47:313-327
[44]Birnbaum M H,Coffey B A,et al.Utility measurement:Configural weight theory and the judges point of view[J] Journal of Experimental Psychology:Human perception and Performance,1992,18(2):331-346
[45]Barrett L F,Tugade M M,Engle R W.Individual differences in working memory capacity and dual process theories of the mind[J]. Psychological Bulletin,2004,130:553-573
[46]Paivio A.Mind and its evolution:A dual coding theoretical approach[M].US:Lawrence Erlbaum Associates Publishers,2007
[47]Kahneman D.A perspective on judgment and choice [J].American Psychologist, 2003.58:697-720
[48]Fagley N S,Miller P M. Framing effects and arenas of choice:Your money or your life?[J].Organizational Behavior and Human Decision Processes,1997,3:355-373
[49]何贵兵,梁社红,刘剑.风险偏好预测中的性别差异和框架效应[J].应用心理学,2002,8(4)：19-23
[50]Harbaugh W T.Krause K,Vesterlund L.Risk attitudes of children and adults:Choices over small and large probability gains and losses[J].Experimental Economics,2002,5:53-84
[51]Hsee C K,Weber E U. A fundamental prediction error:Self-other discrepancies in risk preference[J] Journal of Experimental Psychology:General,1997,126(1):45-53
[52]刘永芳,陈雪娜,卢光莉等.决策者角色及相关因素对风险偏好的影响[J].心理科学,2010,33(3)：548-551
[53]Lauriola M.Russob P M,Lucidib F,et al.The role of personality in positively and negatively framed risky health decisions[J].Personality Individual Difference,2005, 38:45-59
[54]张银玲,苗丹民,罗正学等.正负信息框架下人格特征对决策的影响作用[J].第四军医大学学报,2006,27(4)：363-366
[55]柯青松.时间压力对偏好反转的影响[J].山东理工大学学报(社会科学版),2009,25(3)：94-97
[56]张凤华.不同模糊程度下模糊决策的决策偏好[J].心理学探新,2009,29(6)：51-55
[57]Hollenbeck J K,Ilgen D R,Philips J M,et al.Decision risk in dynamic contexts:Beyond the statusquo[J]. Journal of Applied Psychology,1994,79:592-597
[58]王重鸣,梁立.风险决策中动态框架效应研究[J].心理学报,1998,30(4)：394-400
[59]Paul Resniek,HalR.Varian. Recommender Systems[J]. Communications of the ACM, 1997,40(3):56-58
[60]林平忠.论图书馆信息服务的个性化问题[J].中国图书馆学报,2003,(6)：46-49
[61]冯翱.基于Agent的个性化信息过滤系统Open Bookmark[J]清华大学学报(自然科学版),2001,4(3)：85-88
[62]Richard T. Grenci and Peter A. Todd. Solutions-Driven Marketing[J]. COMMUNICATIONS OF THE ACM,2002,45(3):65-71
[63]Efrem G. Mallach. Decision Support and Data Warehouse Systems[M]. McGraw-Hill Higher Education,2000
[64]Alter S. Decision Support Systems:Current practice and Continuing Challenge[M]. Reading,MA:Addison-Wesley,1980
[65]Klein M,Methlie L B. Knowledge-based Decision Support Systems with Applications in Business[M]. UK:John Wiley&Sons,1995
[66]路云,吴应宇,达庆利.基于案例推理技术的企业经营决策支持模型设计[J].中国管理科学,2005,13(2)：81-87
[67]赵卫东,盛昭瀚,何建敏.决策支持系统的主动性研究[J].管理工程学报,2001,15(1)：36-40
[68]黄清和,尹琪.知识发现与决策支持[J].南开管理评论,2002,(3)：76-78
[69]Jhon W,Payne. The Adaptive Decision Maker[M]. NY:Cambridge University Press,1993
[70]Richard Alba. Rethinking Assimilation theory for a new Era of Immigration[J]. IMR,1997,31(4):826-874
[71]Dale F. Duhan, Scott D. Johnson. Influences on consumer use of word-of-mouth recommendation sources[J].Journal of the Academy of Marketing Science,1997, 25(4):283-295
[72]柴永红.论信息服务与知识服务[J].情报杂志,2004,(4)：74-75
[73]赵杨.基于语义网格的数字图书馆知识推送服务系统研究[J].情报科学,2007,25(12)：1869-1882
[74]FAN Zhiping, FENG Yong, SUN Yonghong, et al. A frame work on compound knowledge push system oriented to organizational employees [J]. Lecture Notes in Computer Science,2005,38(28):622-630
[75]冯勇,樊治平,冯博.企业客户服务中心知识推送系统构建研究[J].计算机集成制造系统,2007,13(5)：1015-1019
[76]江丽萍,康平立.知识型组织中基于本体的知识推送系统研究[J].情报杂志,2007,(8)：79-81
[77]潘旭伟,顾新建,程耀东.集成情境的知识管理模型[J].计算机集成制造系统,2006,12(2)：225-230
[78]周明建,陶俊才.知识管理系统中的知识推送[J].计算机辅助设计与图形学学报,2006,18(8)：1218-1223
[79]Burke R.R. Technology and the Customer Interface:What Consumers Want in the Physical and Virtual Store[J] Journal of the Academy of Marketing Science,2002, 30(4):411-432
[80]Bo Xiao and Izak Benbasat.E-Commerce Product Recommendation Agents:Use, Characteristics, and Impact[J]. MIS Quarterly,2007,31(1):137-209
[81]Daniel Baier & Eva Stuber.Acceptance of recommendations to buy in online retailing[J]. Journal of Retailing and Consumer Services,2010,17:173-180
[82]DeLone, W., and McLean, E. The DeLone and McLean Model of Information System Success:A Ten-Year Update[J] Journal of Management Information Systems,2003, 19(4):9-30
[83]Bharati,P.,and Chaudhury, A. An Empirical Investigation of Decision-Making Satisfaction in Web-Based Decision Support Systems [J].Decision Support Systems,2004,37(2): 187-19
[84]Sinha, R.,and Swearingen, K.Comparing Recommendations Made by Online Systems and Friends[C].In Proceedings of the 2nd DELOS Network of Excellence Workshop on Personalization and Recommender Systems in Digital Libraries, Dublin, Ireland, 2001,18-20
[85]Sinha, R., and Swearingen, K. The Role of Transparency in Recommender Systems[C]. In CHI 2002 Extended Abstracts on Human Factors in Computing Systems, Minneapolis, MN,2002,830-831
[86]Wang, W., and Benbasat, I. Impact of Explanations on Trust in Online Recommendation Agents.Working Paper No.02-MIS-002, MIS Division, Sauder School of Business, University of British Columbia, Vancouver,2004a
[87]Sylvain Senecal, Jacques Nantel. The influence of online product recommendations on consumers'online choices[J]. Journal of Retailing,2004,80:159-169
[88]Savage,I.R. Contributions to the Theory of Rank Order Statistics the Two-sample Case Ann[J].Math.Statist,1956,(27):590-615
[89]邵希娟,杨建梅.行为决策及其理论研究的发展过程[J].科技管理研究,2006,(5)：203-205
[90]普劳斯,施俊琦,王星(译).决策与判断[M].北京：人民邮电出版社,2004
[91]李怀祖.决策理论导引[M].北京：机械工业出版社,1993
[92]Edwards W.Behavioral Decision Theory[J].Annual Review of Psychology,1961, (12): 473-498
[93]Tversky A,Kahneman D.Judgment Under Uncertainty:Heuristics and Biases[J]. Science, 1974,(185):1124-1131
[94]West, H, J Albaum, G.Modern marketing Thought(3rdEd)[M].Collier Macrmillan Publishers,1974
[95]Barry·T·E, Howard·D. A review and critique of the hierarchy of effects in advertising[J]. International Journal of Advertising,1990,9:124
[96]Festinger, L. A Theory of Cognitive Dissonance[M]. Row,Peterson and Company,1957
[97]Enagel·J·F, Blackwell·R·D. Consumer Behaviour(4thed)[M].Dryden,1982
[98]Mead·G·H·Mind.Self and Society[M]. Chicago:University of Chicago Press,1934
[99]Goffman·E. The Presentation of Self in Everyday life[M].Douleday Anchlor Books,1959
[100]Festinger.L,Schachter.S, Back.K. Social Pressure in Informal Groups:A Study of Human Factor in Housing[M].New York:HarperandRow,1963
[101]Thompson S H,Yeong Y D.Assessing the consumer decision process in the digital marketplace [J]. The International Journal of Management Science,2003,31(5):349-363
[102]欧阳洁.决策管理——理论,方法,技巧与应用[M].广州：中山大学出版社,2003
[103]张建鲁,葛禄青,吴军.电子商务对消费者行为的影响分析[J].企业经济,2004,(5)： 69-70
[104]刘丽华.虚拟市场与消费者网络购买流程研究[J].商场现代化,,2008,(10)：69-70
[105]张猛.网络购买过程研究[J].商业营销,2009,(12)：175-176
[106]桂世权.心理学[M].成都：西南交通大学出版社,2006
[107]Haubl GTrifts V.Consumer Decision Making in Online Shopping Environments:The Effects of Interactive Decision Aids[J]. Marketing Science,2000,19(1):4-21
[108]汪青云.营销心理与实务[M].广州：暨南大学出版社,2002
[109]宋奇.行为决策理论研究综述[J].首都经济贸易大学学报,2010,(4)：85-91
[110]王曼,白玉苓,王智勇.消费者行为学[M].北京：机械工业出版社,2007
[111]江晓东,高维和,魏敏菁.消费者品牌决策影响因素研究,华东经济管理,2009,2(10)：115-120
[112]Lascu, D. N.,& Zinkhan, G.Consumer Conformity:Review and Application for Marketing Theory and Practice[J] Journal of Marketing,1999:1-11
[113]李文忠,杜建刚.从众理论及其在营销领域研究发展述评[J].商业研究,2009,(384)：164-168
[114]Meyers-Levy,J.& Peracchio,L.A. Understanding the Effects of Color:How the Correspondence between Available and Required Resources Affects Attitudes[J]. Journal of Consumer Research,1995,22(2):121-138
[115]李东进.消费者搜寻信息努力与影响因素的实证研究[J].南开管理评论,2000,(4)：52-59
[116]黄沛,范小军.主观知识和客观知识对购买决策影响的实证研究[J].系统工程理论方法应用,2002,(4)：265-269
[117]Mai,G.R, and J.M.Olson., Relations Between Values, Attitudes, and Behavioral Intentions:The Moderating Role of Attitude Function[J]. Journal of Experimental Social psychology,1995.31:266-285
[118]林建煌.消费者行为[M].北京：北京大学出版社.2004
[119]张攀春.消费者偏好的无差异曲线分析[J].商业经济,2010,(22)：16-17
[120]肖兴政,李永华.性别差异与设计心理分析.国际中华神经精神医学杂志,2003,4(3)：36-39
[121]S.Messick. Individuality in learning[M].San francisco:Jossey-Bass,1978
[122]Maria K.Cognitive Styles in the Context of Modern Psychology:Toward an Integrated Framework of Cognitive Style[J]. Psychological Bulletin,2007,133(3):464-481
[123]黄丽红.基于认知类型的个性化数字图书馆设计[J].图书馆学研究,2010：50-53
[124]Cacioppo,J.T.,&Petty,R.E. The need for cognition[J] Journal of Personality and Social Psychology,1982,42(1):116-131
[125]Dickhauser,O., Reinhard,M.,Diener,C.,&Bertrams,A. How need for cognition affects the processing of achievement-related information[J]. Learning and Individual Differences,2009,19:283-287
[126]Rajueta RobertB. The effects of product class knowledge on information search behavior [J]. Journal of Consumer Research,1995,22:1-16
[127]黄志诚.信息,知识与情报[J].情报杂志,1996,15(3)：21-22,28
[128]王众托.知识系统工程[M].北京：科学出版社,2004
[129]王甦,汪安圣.认知心理学[M].北京：北京大学出版社,1992
[130]彭聃龄.普通心理学[M].北京：北京师范大学出版社,2004
[131]熊素红,景奉杰.冲动性购买影响因素新探与模型构建[J].外国经济与管理理,2010,32(5)：56-64
[132]DittmarH.,BeattieJ.andFrieseS.Gender identity and material symbols:Objeets and decision considerations in impulse purchases [J] Journal of Economic Psychology, 1995:491-511
[133]李亚林,景奉杰.基于冲动性购买诱发因素的消费者冲动性购买之购后满意度研究[J].管理学报.2012,9(3)：437-445
[134]朱莉莉.同城与异地对冲动性购买行为的影响研究[J].黑龙江对外经贸,2009,(2)：111-113
[135]冯丽云.现代市场营销学[M].北京：经济管理出版社,1998
[136]朱明侠,李盾.网络营销[M].北京：对外经济贸易大学出版社,2002
[137]philipKotier.科特勒市场营销教程[M].北京：华夏出版社,2001
[138]吴照云.市场营销学[M].北京：经济管理出版社,2001
[139]威廉：D皮诺特.营销精要[M].北京：北京大学出版社,2002
[140]林鸿飞,杨元生.用户兴趣模型的表示和更新机制[J].计算机研究与发展,2002,39(7)：843-847
[141]Fishbein M,Ajzen I.Belief.Attitude,Intention and Behavior:an Introduction to Theory and Research[M]. Reading,MA:Addison-Wesley,1975
[142]Katz D.The Function Approach to the study of Attitudes[J]. Public Opinion Quarterly,1960,24:163-204
[143]Sheppard B H,Hartwick J,Warshaw P R. The Theory of Reasoned Action:A Meta-analysis of Past Research with Recommendations for Modifications and Future Research[J]. Journal of Consumer Research,1988,15:325-343
[144]ArmstrongJS,MorwitzVG,KumarV. Sales Forecasts for Existing Consumer Products and Services:Do Purchase Intentions Contribute to Accuracy?[J]. International Journal of Forecasting,2000,16:383-397
[145]Pennock, D. and Horvitz.E. Collaborative Filtering by Personality Diagnosis:A Hybrid Memory and Model-based Approach[C]. In Proceedings of UAI,2000,473-480
[146]李秦.基于用户行为的全文检索系统个性化推荐研究[D].重庆：西南大学硕士论文,2009
[147]赵银春等.基于Web浏览内容和行为相结合的用户兴趣挖掘[J].计算机工程,2005,31(12)：93-94
[148]孙铁利,杨凤芹.根据用户隐式反馈建立和更新用户兴趣模型[J].东北师大学报自然科学版,2003,35(3)：99-104
[149]张玉连,王权.基于浏览行为和浏览内容的用户兴趣建模[J].现代图书情报技术,2007(6)：52-55
[150]R.Agrawal.T.Imielinski,and A.Swami.Mining Association Rules Between Sets of Items in Large Databases[C].Proc.1993 ACM SIGMOD Int'l Conf. Management of Data, Washington,D.C.,1993,207-216
[151]R.Agrawal,R.Srikant.Fast Algorithms for Mining Association Rules in Large Databases[C].In Proc.1994 Intl.Conf.On Very Large Databases(VLDB'1994), Santiago,Chile,1994,487-499
[152]D.W.Cheung V.T.Ng W.Fu Y.Fu.Efficient Mining of Association Rules in Distributed Databases [J].IEEE Transactions on Knowledge and Data Engineering,1996,8(6): 911-922
[153]陈定权,朱维凤.关联规则与图书馆书目推荐[J].情报理论与实践,2009,32(6)：81-84
[154]雷蕾.基于关联规则的个性化图书推荐研究[J].情报探索,2011(1)：49-50
[155]刘旭东.B2C网上购物推荐系统的设计与实现[J].计算机应用与软件2009,26(9)：195-197
[156]赵妍,苏玉召,管涛.一种提高过滤用户偏好精度的数据采集方法[J].现代图书情报技术,2011,(11)：31-37
[157]常虹.数码产品设计中情感价值的应用研究[D].浙江：浙江农林大学硕士论文,2010
[158]黄华.便携式消费电子产品需求特征分析[D].上海：东华大学硕士论文.2009
[159]严璇.基于视觉心理学理论对数码相机的设计和研究[D].西安：西安建筑科技大学硕士论文,2009
[160]杨汝全.都市青年数码相机设计体验心理研究[D].无锡：江南大学硕士论文,2007
[161]陈国龙.涉入理论及其衡量[J].商业时代,2007,(3)：69-71
[162]周欣,沙朝锋,朱扬勇,施伯乐.兴趣度—关联规则的又一个阀值[J].计算机研究与发 展,2000,37(5)：627-633
[163]Jacob Nielsen,刘正捷等译.可用性工程(Usability Engineering) [M].北京：机械工业出版社,2004
[164]Alain A, Adel K,Witold,S. Usability Meanings and Interpretations in ISO Standards[J]. Software Quality Journal,2003,11(4):325-338
[165]Harston.HR. Human-computer interaction:interdisciplinary roots and trends[J]. The Journal of System and Software,1998,43:103-118
[166]王新宇,余明阳.基于消费者信任行为的品牌危机动态演化机制研究[J].现代管理科学,2010(6)：8-10
[167]邵兵家.消费者社交网站购物态度影响因素的实证研究[J].情报杂志,2010,29(8)：203-207
[168]丁学君.BtoC模式下移动商务用户接受行为研究[J].电子商务,2010：16-19
[169]David Gefen.E-commerce:the role of familiarity and trust[J].Omega:The International Jounal of Management Science,2000,28:725-737
[170]ZUCKER L.Production of trust institutional sources of economic structure [J].Research in Organizational Behavior,1986,8(1):53-111
[171]Teo T S H,Lim V K G,Lai R Y C.Intrinsic and Extrinsic Motivation in Internet Usage[J]. International Journal of Management Science,1999,27:25-37
[172]张凤玲.基于TAM的旅游电子商务用户使用行为研究[J].中国商贸,2010,(10)：104-105
[173]鲁耀斌,徐红梅.技术接受模型的实证研究综述[J].研究与发展管理,2006(6)：93-99
[174]窦军生.计划行为理论及其在家族企业传承研究中的应用[J].技术经济,2007(2)：39-42
[175]Hong S J,Thong JY L,TamK Y. Understanding Continued Information Technology Usage Behavior: A Comparison of Three Models in the Context of Mobile Internet [J].Decision Support Systems,2006(42):1819-1834
[176]Herlocker, J., Konstan, J. A., and Riedl, J. Explaining Collaborative Filtering Recommendations[C]. in Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work Philadelphia, PA,2000,241-250
[177]Pereira, R. E. Optimizing Human-Computer Interaction for the Electronic Commerce Environment. Journal of Electronic Commerce Research,2000,1(1):23-44
[178]K.S.McGrew. CHC Theory and the Human Cognitive Abilities Project:Standing on the Shoulders of the Giants of Psychometric Intelligence Research[J].Intelligence, 2009,37(1):1-10
[179]Enrique Frias-Martinez. Automatic Cognitive Style Identification of Digital Library Users for Personalization[J]. Journal of The American Society For Information Science And Technology,2007,58(2):237-251