随着智能手机的普及,移动网络环境的升级,移动互联网时代已经全面降临。用户

可以通过丰富的应用在移动端实现通信、社交、搜索、娱乐等功能。由于手机的便携性

和实时在线的特性,近一两年来,用户对于手机的依赖和使用时间大幅提升。传统的电

子商务公司也将重心全面转向移动端,纷纷推出自己的移动客户端。越来越多的用户通

过手机进行商品的浏览、购买。移动电子商务应用所占整体业务的比重快速提高。但由

于信息过载的问题,用户难以通过传统的搜索找到合适的商品。商品推荐作为解决信息

过载重要的手段,记录用户的历史和实时行为,分析用户习惯和喜好,为用户进行个性

化推荐。

本文通过深入研究国内外与推荐系统相关文献,对品牌信任、可靠性、方便性、交

互、界面这些影响用户接受推荐的关键要素研究。结合信息接受理论,涉入度理论,认

知差异理论建立研究模型。本文以S公司的推荐系统和用户人群为研究样板。设计结构

化量表问卷和回收。利用SPSS和AOMS软件对模型和统计数据进行实证分析,验证假

设模型是否成立。

经过研究发现:涉入高-适应型、涉入高-创新型、涉入低-适应型、涉入低-创新型,

在品牌信任、可靠性、方便性、交互、界面影响因素上存在明显的区别。根据研究结论,

本文对移动推荐系统提出了一定的优化建议。

关键词:移动电子商务;推荐系统;技术接受模型;涉入度;认知差异;

第II页
华东理工大学硕士学位论文

Research on key elements of mobile device users accepting

merchandise recommendation——Taking S company's

Mobile terminal Business Application for example

Abstract

With the popularity of smart phones, upgrade of mobile network environment, the

mobile network ear has come all-round. Users can realize the communication, social contacts,

search, entertainment and other functions through multiple Apps on mobile terminals. As

mobile phones are very portable and can be real-time online, uses have become more

dependent and been spending much more time on them in recent one or two years. Traditional

e-commerce companies have also switched all their focus on mobile terminals by developing

their own mobile applications. More and more users browse commodity information and

purchase via mobile phones. Mobile e-commerce applications are growing rapidly among

overall business. But due to the problem of information overload, users are difficult to find

appropriate goods through traditional search. Merchandise recommendation, as a solution

for information overload, can make personalized recommendations for uses by recording their

history data and real-time activities, analyze their habits and preferences.

This paper, through intensive study of the related domestic and overseas literatures, makes

research on those key elements of the users' acceptance of goods recommendation such as

brand trust,reliability, convenience, interaction, and interface. This paper also develops the

research model by combining information acceptance theory, involvement theory, and

cognition difference theory. Last, this paper designs structured questionnaire, makes empirical

analysis of the model and statistical data by using SPSS and AOMS software, and verify if the

hypothesis model is established.

Through study we found that: More-involved adaptors model, More-involved innovators

model, Less-involved innovators model, Less-involved adaptors model, have much different

attitudes on brand trust, reliability,convenience, interaction, and interface. This paper makes

suggestions on the optimization of mobile recommendation system.

Keywords: Mobile e-commerce; Recommendation system; Technology acceptance model;

Involvement; Cognitive different;