【作者】 王琛；

【导师】 李赤林；

【作者基本信息】 武汉理工大学 ， 管理科学与工程， 2016， 硕士

【摘要】 电子商务规模的迅猛发展,使得电子商务平台的信息资源数量不断增加,电子商务平台顾客会有更多机会选择商品,同时也会花费更多时间筛选信息,增加获取真正需要信息的难度。为了解决顾客这方面的问题,电子商务推荐系统应运而生。电子商务推荐系统的功能在于减轻信息过载给顾客带来的影响,帮助顾客找到感兴趣的商品。但是,目前很多电子商务顾客对推荐技术持有消极的态度,如何增强顾客采纳意愿成为推荐服务商需要考虑的首要问题。根据推荐系统的特征,探索推荐系统顾客采纳意愿的关键影响因素,针对这些影响因素提出提高推荐系统顾客采纳意愿的措施,以此来实现电子商务网站个性化推荐服务质量的提高。以感知价值为切入点,结合电子商务推荐系统的特征,研究影响顾客感知价值的关键因素,以及顾客感知价值对推荐系统采纳意愿的影响。在借鉴国内外学者相关研究成果的基础上,从推荐系统特征角度选取了顾客感知价值的前因变量,即推荐准确性、推荐多样性、推荐可信性以及系统交互性,同时提出了电子商务推荐系统顾客感知价值的维度,即功能价值、情感价值以及社会价值。在此基础上,建立了电子商务推荐系统特征-顾客感知价值-采纳意愿的理论模型,并提出相关假设,包括推荐准确性、推荐多样性、推荐可信性和系统交互性对顾客感知价值维度的影响,以及顾客感知价值维度对电子商务推荐系统采纳意愿的影响。为验证模型以及假设是否正确,将推荐准确性、推荐多样性、推荐可信性、系统交互性、功能价值、情感价值、社会价值以及采纳意愿作为调查变量,充分考虑电子商务推荐系统的特征,为各个调查变量设计相应的测量题项,形成调查问卷。通过对有电子商务推荐系统使用经验的顾客进行问卷调查,对问卷数据进行信度和效度分析以及结构方程模型分析,并对本文所提出的假设进行校验。研究结果表明,推荐准确性、推荐多样性、推荐可信性和系统交互性对顾客感知价值有显著影响,顾客感知价值对推荐系统采纳意愿有显著正向影响。以此结论为依据提出了提高推荐系统顾客采纳意愿的策略与建议,为促进电子商务个性化推荐服务快速发展提供了理论指导。更多还原

【Abstract】 The rapid development of e-commerce provides customers more choice opportunities in commodities,but also brings customers real difficulty obtaining needed information,increasing the cost of search for commodities.In order to solve this problem,e-commerce recommendation system came into being.E-commerce recommendation system is to alleviate the impact of information overload bringing to customers,help customers find commodities in need.However,a lot of e-commerce customers hold a negative reaction to recommendation system,how to enhance customers’ intention to adopt recommendation system is the most important issue.The ultimate goal of this paper is to identify the factors of impacting customers’ adoption of e-commerce recommendation system,for these factors this paper proposes measures to improve customers’ adoption of e-commerce recommendation system,in order to improve personalized recommendation service quality of e-commerce site and customers’ experience.In this paper,the perceived value as a starting point,the binding characteristics of e-commerce recommendation system to study factors affecting customers’ perceived value,and on this basis to study the impact degree of customers’ perceived value to the adoption intention.On the basis of related scholars’ research,this paper selects key characteristics elements of recommendation system,including recommended accuracy,recommended diversity,recommended credibility and system involvement,also proposes dimensions of perceived value to the e-commerce recommendation system,including functional value,emotional value and social value.On this basis,the paper builds the theoretical model about characteristics of e-commerce recommendation system,customers’ perceived value and customers’ adoption intention,also makes relevant assumptions,including the impact of recommended accuracy,recommended diversity,recommended credibility and system involvement to customers’ perceived value,and the impact degree of customers’ perceived value to the adoption intention.To verify the model and the assumptions are correct,this paper regards recommended accuracy,recommended diversity,recommended credibility,system involvement functional value,emotional value,social value and adoption intention as survey variables.After fully considering the characteristics of e-commerce recommendation system,this paper designs corresponding measurement questions for each survey variable in order to form questionnaire.Through making questionnaire survey for customers with experience using e-commerce recommendation system,this paper makes reliability analysis,validity analysis and structural equation modeling analysis of questionnaire data,and checked proposed assumptions.The results show that recommended accuracy,recommended diversity,recommended credibility and system involvement have significant influences on customers’ perceived value,and customers’ perceived value has a significant positive influences on adoption intention.According to the conclusion,this paper proposes strategies and methods to improve customers’ adoption intention to e-commerce recommendation system,for theoretical guidance of rapid development of e-commerce personalized recommendation service.更多还原