Recommended To Resist Consumer Behavior Of The Network

In recent years, China's online shopping presents a good developing trend,while a rich variety of network products increases consumers' difficulties in theirshopping decisions, and the problem of information overload is becoming more andmore apparent. The major e-commerce websites all have developed their ownnetwork recommender system to help consumers retrieve and compare from vastamounts of goods, and to recommend products that meet consumer demands basedon their preferences and other personal information. From the point of view ofconsumers' resist behavior towards network recommendations, the study byexperimental methods analyzes the factors that may affect the networkrecommendations and the relationship between various factors, and find appropriatesolutions to provide the scientific basis of development of network recommendersystem for online sellers.In this study, based on the reference, we divide the network recommendationsinto two types: non-personalized recommendations and personalizedrecommendations. Putting technology acceptance model (Technology AcceptanceModel, TAM) as the basic framework, we bring the network recommendation trust,the perceived value of network recommendation as intermediary variables into thismodel, and bring the characteristics of online consumers, the recommendationplatform features and the quality of recommended information as three categories ofexternal variables into the model to explore the relationship between the externalvariables and the network recommendation trust, and the perceived value of networkrecommendation, and to explore the relationship between the networkrecommendation trust, the perceived value of network recommendation andconsumers' resist behavior towards network recommendations. Through simulationpurchasing made online, in the questionnaire survey satellite network platform(http://www. sojump.com) we collect318valid questionnaires. That as a sample,with SPSS17.0statistical software, collected data is analyzed by descriptivestatistical analysis, single-factor analysis, correlation analysis, and multiple linearregression analysis; meanwhile the model is verified and corrected.In this study, through experimental researches and analysis we can reach to the following conclusions: consumers were more resist towards personalizedrecommendations than non-personalized recommendations, and there is somemisunderstanding of their perceiving of personalized recommendation; Onlineshopping frequency, professional abilities, recommended platform features,consumer culture tend have a great positive impact on the perceived value of thenetwork recommendations; recommended platform characteristics, expertise,consumer culture tend have a great positive influence on the networkrecommendations trust; perceived value of network recommendations have a greatreverse effect to the consumers' resist behavior towards network recommendations;the network recommendation trust has a weak reverse impact on the consumers'resist behavior towards network recommendations. Based on the above findings, wecan offer the relative marketing recommendations: enhancing the consumers'online shopping experience; providing customization of personalizedrecommendations; focusing on the consumers who go online shopping with highfrequency and high professional ability as the key customer base.