The progress of IT technology such as social network and mobile payment and the change of social economic environment promote the emergence of sharing economy.As a subversive business model,the sharing economy is grow...The progress of IT technology such as social network and mobile payment and the change of social economic environment promote the emergence of sharing economy.As a subversive business model,the sharing economy is growing at an alarming rate all over the world.However,the influencing factors of consumers'continuous participation in the sharing economy are not clear.The paper aims to clarify the relationship between consumer perceived value and repeat purchase intention in the sharing economy.Taking the sharing economy platform(Airbnb)as an example,it proposes a dimension framework of consumer perceived value in peer-to-peer(P2P)accommodation rental service,including functional value,hedonic value,epistemic value and social relationship value.This paper used big data technology to crawl online reviews of P2P accommodation platform.LDA(Latent Dirichlet Allocation)topic model and sentiment analytics method were applied to construct the measurement indicators of perceived value based on online reviews.And repeat purchase intention variables were extracted from online reviews.Then structural equation model was used to examine the effect of perceived value dimensions on it.The paper identified that perceived value has a positive impact on consumers'repurchase intention in P2P accommodation.Also,social relationship value was considered as the most important influencing factor.展开更多
With the rapid growth of online shopping platforms, more and more customers intend to share theirshopping experience and product reviews on the Internet. Both large quantity and various forms ofonline reviews bring di...With the rapid growth of online shopping platforms, more and more customers intend to share theirshopping experience and product reviews on the Internet. Both large quantity and various forms ofonline reviews bring difficulties for potential consumers to summary all the heterogenous reviews forreference. This paper proposes a new ranking method through online reviews based on differentaspects of the alternative products, which combines both objective and subjective sentiment values.Firstly, weights of these aspects are determined with LDA topic model to calculate the objectivesentiment value of the product. During this process, the realistic meaning of each aspect is alsosummarized. Then, consumers＇ personalized preferences are taken into consideration while calculatingtotal scores of alternative products. Meanwhile, comparative superiority between every two productsalso contributes to their final scores. Therefore, a directed graph model is constructed and the finalscore of each product is computed by improved PageRank algorithm. Finally, a case study is given toillustrate the feasibility and effectiveness of the proposed method. The result demonstrates that whileconsidering only objective sentiment values of the product, the ranking result obtained by our proposedmethod has a strong correlation with the actual sales orders. On the other hand, if consumers expresssubjective preferences towards a certain aspect, the final ranking is also consistent with the actualperformance of alternative products. It provides a new research idea for online customer review miningand personalized recommendation.展开更多
基金Evolution Trend and Coping Strategies of Online Public Opinions on Emergencies Based on Big Data Analysis(Grant No.:18CHLJ22)Social Science Planning Project in Shandong Province+1 种基金“Online Public Opinion Concerning Ethnic Factors Based on Big Data Analysis”(Grant No.:2018-GMB-022)Ethnic Research Project of State Ethnic Affairs Commission in China.
文摘The progress of IT technology such as social network and mobile payment and the change of social economic environment promote the emergence of sharing economy.As a subversive business model,the sharing economy is growing at an alarming rate all over the world.However,the influencing factors of consumers'continuous participation in the sharing economy are not clear.The paper aims to clarify the relationship between consumer perceived value and repeat purchase intention in the sharing economy.Taking the sharing economy platform(Airbnb)as an example,it proposes a dimension framework of consumer perceived value in peer-to-peer(P2P)accommodation rental service,including functional value,hedonic value,epistemic value and social relationship value.This paper used big data technology to crawl online reviews of P2P accommodation platform.LDA(Latent Dirichlet Allocation)topic model and sentiment analytics method were applied to construct the measurement indicators of perceived value based on online reviews.And repeat purchase intention variables were extracted from online reviews.Then structural equation model was used to examine the effect of perceived value dimensions on it.The paper identified that perceived value has a positive impact on consumers'repurchase intention in P2P accommodation.Also,social relationship value was considered as the most important influencing factor.
文摘With the rapid growth of online shopping platforms, more and more customers intend to share theirshopping experience and product reviews on the Internet. Both large quantity and various forms ofonline reviews bring difficulties for potential consumers to summary all the heterogenous reviews forreference. This paper proposes a new ranking method through online reviews based on differentaspects of the alternative products, which combines both objective and subjective sentiment values.Firstly, weights of these aspects are determined with LDA topic model to calculate the objectivesentiment value of the product. During this process, the realistic meaning of each aspect is alsosummarized. Then, consumers＇ personalized preferences are taken into consideration while calculatingtotal scores of alternative products. Meanwhile, comparative superiority between every two productsalso contributes to their final scores. Therefore, a directed graph model is constructed and the finalscore of each product is computed by improved PageRank algorithm. Finally, a case study is given toillustrate the feasibility and effectiveness of the proposed method. The result demonstrates that whileconsidering only objective sentiment values of the product, the ranking result obtained by our proposedmethod has a strong correlation with the actual sales orders. On the other hand, if consumers expresssubjective preferences towards a certain aspect, the final ranking is also consistent with the actualperformance of alternative products. It provides a new research idea for online customer review miningand personalized recommendation.
