Applying virtual reality for trust-building e-commerce environments

The application of virtual reality in e-commerce has enormous potential for transforming online shopping into a real-world equivalent. However, the growing research interest focuses on virtual reality technology adoption for the development of e-commerce environments without addressing social and behavioral facets of online shopping such as trust. At the same time, trust is a critical success factor for e-commerce and remains an open issue as to how it can be accomplished within an online store. This paper shows that the use of virtual reality for online shopping environments offers an advanced customer experience compared to conventional web stores and enables the formation of customer trust. The paper presents a prototype virtual shopping mall environment, designed on principles derived by an empirically tested model for building trust in e-commerce. The environment is evaluated with an empirical study providing evidence and explaining that a virtual reality shopping environment would be preferred by customers over a conventional web store and would facilitate the assessment of the e-vendor’s trustworthiness.     

用户新闻信息浏览行为和感知价值研究

随着互联网技术的发展,用户信息行为的研究越来越受到重视,浏览行为是用户信息行为的重要组成部分,浏览行为的研究对提升用户体验具有重要意义.以今日头条用户为研究对象,基于顾客感知价值理论,使用访谈法与问卷调查法相结合的方法,探索影响用户浏览新闻的行为的因素.研究发现,用户在移动端浏览新闻场景下,只有实用价值对新闻浏览行为正...     

新客户在商务站点中的个性化推荐

提出了一种针对新客户在商务站点购物的个性化推荐方法。首先利用已购物客户的浏览信息生成购物行为模型,得到新客户在站点中的浏览行为生成浏览行为模型,通过最近邻居的协同过滤技术生成与新客户行为最为相近的用户集,将最近邻居已购商品推荐给新客户。该方法能够给新客户提供及时准确的个性化商品信息。     

结合使用挖掘和内容挖掘的web推荐服务

随着Internet的基础结构不断扩大和其所含信息的持续增长,Internet用户越来越感觉容易在WWW服务中“资源迷向”。提高用户访问效率的方法有页面预取技术,站点动态重构技术和web个性化推荐技术等。现有的大多数web个性化推荐技术主要是基于用户使用记录的数据挖掘方法,没有或很少考虑结合页面内容—这才是用户真正感兴趣的。该文提出一种结合用户使用挖掘和内容挖掘的web推荐服务,该推荐服务根据频繁最大前向访问路径,提出含有导航页和内容页的频繁访问路径图概念,根据滑动窗口内的最近用户访问页面内容和候选推荐集中页面内容相关性,来向用户提供个性化推荐服务。经推荐质量分析,这种方法具有较好的推荐优化能力。     

基于数据挖掘的移动广告个性化推荐研究

移动互联网目前已经成为当今最具潜力的市场之一,与此同时,移动广告作为一种新兴的网络营销模式也得以迅速发展起来。诸多企业已经把目标投放在移动广告市场这一领域。当前,移动广告主要利用传统的广告定位策略,通过应用程序展示触发客户需求并以此来进行传播,从而导致移动用户被动地接受一些并不感兴趣的广告内容,严重干扰了用户的正常使用,甚至会泄露用户的个人隐私。文中从分析用户个人的行为和如何定位用户的实时位置出发,研究基于移动互联网下广告个性化推荐策略,提出研究模型,开发基于用户位置和兴趣的实时广告推荐服务系统。     

Item-level RFID for enhancement of customer shopping experience in apparel retail

In the customer-oriented apparel retail industry, providing satisfactory shopping experience for customers is a vital differentiator. However, traditional stores generally cannot fully satisfy customer needs because of difficulties in locating target products, out-of-stocks, a lack of professional assistance for product selection, and long waiting for payments. Therefore, this paper proposes an item-level RFID-enabled retail store management system for relatively high-end apparel products to provide customers with more leisure, interaction for product information, and automatic apparel collocation to promote sales during shopping. In this system, RFID hardware devices are installed to capture customer shopping behaviour and preferences, which would be especially useful for business decision-making and proactive individual marketing to enhance retail business. Intelligent fuzzy screening algorithms are then developed to promote apparel collocation based on the customer preferences, the design features of products, and the sales history accumulated in the database. It is expected that the proposed system, when fully implemented, can help promote retail business by enriching customers with intelligent and personalized services, and thus enhance the overall shopping experience.     

Empirical analysis of consumer reaction to the virtual reality shopping mall

The Internet shopping mall has received wide attention from researchers and practitioners due to the fact that it is one of the most killing applications customers can find on the Internet. Though numerous studies have been performed on various issues of the Internet shopping mall, some research issues relating to the user interface of VR (virtual reality) shopping malls still await further empirical investigation. The objective of this study is to investigate whether the user interface of the VR shopping mall positively affects customer satisfaction in comparison with the ordinary shopping mall. For this purpose, we developed a prototype of the VR shopping mall for which the user interface consists of both 3D graphics and an avatar, using it as an experimental medium. 102 valid questionnaires were gathered from active student users of the ordinary shopping mall, and two research hypotheses were then tested to prove whether the three explanatory variables such as convenience, enjoyment, quality assurance improve in the VR shopping mall, and whether customer satisfaction is also significantly enhanced in the VR shopping mall in comparison with the ordinary shopping mall. Additionally, we conducted the PLS (partial least square) analysis to test whether the customer satisfaction is explained significantly by the three explanatory variables or not.     

基于知识库的客户网购意向预测系统

电子商务网站所产生的海量客户网购行为数据中蕴含着丰富的反映客户网购行为规律的知识。这些知识是进行客户网购意向预测以及在此基础上进一步进行商品推荐的重要依据。基于Hadoop框架构建了“客户购买意向预测系统”,实现了利用隐含在交易数据中的知识对正在浏览中的电商客户的购买意向预测。在基于知识图谱技术构建网购客户购买行为知识库的基础上,利用FP-Growth算法和数据统计方法实现了以增量方式从购买者的交易数据中获取反映用户购买规律的知识,并将取得的知识融合到知识库中。系统能够利用存储于知识库中的知识在客户进行网上购物的过程中对其购买意向进行分析和预测,从而达到提高电子商务平台的运营效率,改善客户购物体验的目的。     

Perceived derived attributes of online customer reviews

The influence of online customer reviews (OCRs) on customers' purchase intention has recently gained considerable attention, in both academic and business communities. Technology allows customers to freely and easily post their comments and opinions online about any product or service; this type of customer review can have a significant effect on customers' purchase decisions. Previous studies, however, have mainly focused on the influence of the virtual attributes of OCRs such as volume and valence on consumers' intentions, while limited attention has been paid to understanding the effects of the derived attributes. This study, thus, aims to understand the impact of the perceived derived attributes of OCRs on customer trust and intention. This study develops a – Perceived Derived Attributes (PDA) - model, based on the inclusion of perceived control from the Theory of Planned Behaviour (TPB) with the Technology Acceptance Model (TAM), in order to investigate the effects of OCRs on customers’ purchasing intention. A total of 489 responses to a survey were collected from users of amazon.com. The findings from this study suggest that customer trust in an e-vendor and their intention to shop online are significantly affected by perceived usefulness, perceived ease of use and perceived enjoyment of OCRs. Furthermore, the sense of control derived from OCRs significantly affects customer intention and significantly affects customer trust in e-vendors, particularly for customers who frequently check OCRs before making a purchase. Clearly, those attributes of OCRs are linked to the development of the shopping environment, which consequently can affect sales.     

基于用户浏览行为分析的用户兴趣度计算

从用户的浏览行为可以反映用户的兴趣出发,分析了用户的浏览行为与兴趣之间的关系,通过分析把能反映用户兴趣的典型浏览行为归纳为保存页面、打印页面、将页面加入收藏夹、复制页面内容、访问同一页面的次数和在页面上浏览时间这些浏览行为.针对这些浏览行为下的用户兴趣度计算做出阐述,并且在分析基于网页驻留时间和浏览次数计算网页兴趣度的算法的基础上,考虑网页大小的影响因素,使用基于浏览速度计算网页兴趣度,同时利用BP神经网络进行用户兴趣度融合.     

虚拟空间中社会分层行为研究

大量的人类行为发生在互联网上,互联网已成为与真实空间相对应的最重要的虚拟空间。传统虚拟空间中的社会分层研究基于网络信息资源占有的机会和能力等客观指标,并未涉及用户使用网络资源的具体行为及信息的内容和性质等因素。利用中国互联网络信息中心提供的用户在线行为大数据,从在线时间和上网内容两方面考察并分析了不同阶层的用户在虚拟空间中上网行为的特征和差异性。研究发现不同阶层的用户在虚拟空间中的停留时间和注意力聚焦点都大不相同。较高阶层用户能更好地利用网络资源办公和购物,且在虚拟空间中的停留时间具有相对稳定性。而较低阶层用户将大量的注意力消耗在休闲娱乐类应用上,且停留时间不稳定。此外,本文利用基于word2vec的神经网络模型(W2V-BP),对用户在虚拟空间中的上网行为数据进行社会分层识别,识别准确率达到90.22%,表明虚拟空间中存在能够区分社会分层的行为特征。     

Discovering patterns of online purchasing behaviour and a new‐product‐launch strategy

Spurred by rapid development of computers and Internet technology, online shopping is gradually overtaking in‐store shopping, because of advantages such as convenience, more choice of products or services etc. Online stores must devote a great deal of time and resources to locating and attracting new customers. Growing a customer base requires first understanding customers and then providing the products or services they need, thus encouraging customers to purchase more. This paper develops a system to analyse customers’ purchasing behaviour and track shifts in their interests. Customers’ purchasing behaviour is measured using proposed standard product loyalty status and standard brand loyalty status. Using these metrics, together with the preference map established for each customer, a marketing specialist can easily locate potential customers to target when a company launches a new product. The new‐product‐launch strategy proposed in this paper can be used to create a list of potential customers for a product being launched under a variety of conditions. A prototype system has been built to test the feasibility of the proposed new‐product‐launch strategy. The result shows almost 40% of potential customers respond to the recommendation positively.     

User-centric adaptation of Web information for small screens

A global increase in PDA and cell phone ownership and a rise in the use of wireless services lead mobile browsing to become an important means of Internet access. However, mobile devices have a small screen, which limits the usability of mobile browsing. This paper presents a novel method that automatically adapts a desktop presentation to a mobile presentation, proceeding in two steps: detecting boundaries among different information blocks (i.e., page segmentation) and then generating a user preferred adaptive layout. Distinct from other approaches, our approach analyzes both the DOM structure and the visual layout to detect closely related contents. In the process of page segmentation, our approach first divides a Web page into several common areas (such as top, bottom, and main content), and then further identifies different topics in the main content based on the visual and structural analysis. The page segmentation produces a block tree, which represents the information organization underlying a Web page. Based on the block tree, an adaptive layout, which places closely related information in proximity and minimizes scrolling, is automatically generated. All current approaches support only one fixed style (e.g., a narrow-page style) to render an adaptive layout for a diverse range of users, Websites, and devices. This “one size fits all” strategy could not offer universal usability. Our approach supports a user to flexibly specify the style of an adaptive layout according to his/her personal preferences. The theoretical foundation of such a user-centric adaptive layout is a feature space, which specifies an adaptive layout from different aspects, such as the location and presentation of navigation options. A user can visually manipulate those features to create a personalized style in a graphical user interface, and then an adaptive layout consistent with the personalized style is automatically generated. The user study based on a prototype shows the usability and efficiency of mobile browsing are significantly improved.     

基于JSP技术的网上购物系统

本系统是基于JSP动态网页技术开发的网上购物系统.所开发的基于JSP技术的网上购物系统具有前台用尸操作和后台管理两大模块,前台主要功能包括用户注册、产品订购、购物车查看、订单生成、订单查询、留言查看和留言发布等,后台由管理员使用,主要包括商品添加、订单处理、用户信息管理、添加链接、公告信息添加等.对在线购物方面,用户可查询自己需要的商品,选好商品后加入购物车,确定要买的商品后,可去收银台结帐,生成订单,这样就完成了整个购物流程.基于JSP技术的网上购物系统属于电子商务系统,应用B/S开发模型,其后台应用SQL Server 2000数据库,前台应用ISP开发语言.在JDK和TOMCAT等工具的应用下可方便地对本系统进行测试.在系统设计中应时刻注意系统妥全性,因为电子商务的安全性是非常重要的,必须给用户提供安全性的保证.     

To buy or not to buy online: adopters and non-adopters of online shopping in Singapore

The Internet, as a dynamic virtual medium for selling and buying information, services and products, is gaining increasing attention from researchers and practitioners. In this study, we examine the perceptions of adopters and non-adopters of online shopping in terms of demographic profile, consumer expectations of online stores, advantages and problems of online shopping and transaction cost. In addition, we also examine the types of products purchased, frequency of online purchase and the extent of communication with e-commerce vendors. The findings are useful in explaining consumers' buying behaviour in the electronic marketplace. Implications of the results are discussed.     

基于Web日志的煤矿企业网站个性化推荐服务研究

为了解决煤矿企业网站用户查找信息难的问题,提出了一种基于Web日志的煤矿企业网站个性化推荐服务模型。该模型应用关联规则对新用户进行页面推荐,应用聚类算法对老用户进行页面推荐;并结合点击网页的次数、网页的浏览时间、雅可系数与最长公共路径系数来度量用户兴趣度的方法,可为用户准确地推荐其感兴趣的页面。测试结果表明,该模型能够有效地对网页资源进行分类并进行个性化推荐。     

To buy or not to buy online: adopters and non-adopters of online shopping in Singapore

The Internet, as a dynamic virtual medium for selling and buying information, services and products, is gaining increasing attention from researchers and practitioners. In this study, we examine the perceptions of adopters and non-adopters of online shopping in terms of demographic profile, consumer expectations of online stores, advantages and problems of online shopping and transaction cost. In addition, we also examine the types of products purchased, frequency of online purchase and the extent of communication with e-commerce vendors. The findings are useful in explaining consumers' buying behaviour in the electronic marketplace. Implications of the results are discussed.     

The effect of Internet general privacy concern on customer behavior

Privacy and its related variables are important determinants of customer behavior in the electronic market. This paper aims to examine the concern for customer privacy on the Internet, mainly with regard to disclosing personal information online. Furthermore, by establishing an experimental situation, we will analyze how this behavior is influenced by familiarity with the website sponsor, the level of control over the information the customer has and the sensitivity of the information requested.The results indicate that all the above-mentioned variables (except familiarity) have a significant impact on consumer online behavior.     

Profiling users by online shopping behaviors

Online shopping has been prevalent in our daily life. Profiling users and understanding their browsing behaviors are critical for enhancing shopping experience and maximizing sales revenue. In this paper, based on a one-month dataset recording 2 million users’ 67 million online shopping and browsing logs, we seek to understand how users browse and shop products, and how distinct these behaviors are. We find that there exist dedicate groups of users that prefer certain product categories corresponding to similar demands. Moreover, distinct differences of behaviors exist in categories, where repetitive and targeted browsing are two major prevalent patterns.     

基于FLaAT的模糊用户偏爱浏览模式的增量挖掘

挖掘用户偏爱的浏览模式就是从Web日志中发现多数用户偏爱的浏览路径.网页上的浏览时间被转换成一个模糊语言变量来体现网页上浏览时间的特征,最后从建立的包含所有用户浏览信息的FLaAT(Frequent Link and Access Tree)中挖掘增量式带有模糊语言变量的用户偏爱浏览模式.