{"title":"社交网络的影响:了解消费者的协同购买行为","authors":"Yi Sun, Zhao Pan","doi":"10.1109/LISS.2018.8593231","DOIUrl":null,"url":null,"abstract":"With the popularity of social networking, consumer often relies on social network when making purchase decisions. Despite the growing importance of social network in supporting online purchase behavior, there has been limited research focusing on the effect of social network on consumer collaborative purchase behavior. This paper proposes a framework for understanding consumer’s collaborative purchase behavior within online social groups. It is one of the first studies to our knowledge that explore the combined effect of social network structure and content on consumer’s collaborative purchase. In this study, we first extract and analyze adjacent matrix, which representing related social networks, from a large dataset. This is then combined with interaction-based content analysis to identify the structure of social network. After verifying the impact of network attributes and structure on consumer purchases, we conducted content analysis based on chat records and identified productrelated interaction structures and content composition information based on the language action perspective. Finally, we combine content analysis with network topology analysis to construct a weighted social network and calculate the impact of these social networks on consumer purchasing behavior. Our research proposes a research framework to effectively collect, extract and analyze the structure and content of social networks.","PeriodicalId":338998,"journal":{"name":"2018 8th International Conference on Logistics, Informatics and Service Sciences (LISS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Impact of Social Network: Understand Consumer’s Collaborative Purchase Behavior\",\"authors\":\"Yi Sun, Zhao Pan\",\"doi\":\"10.1109/LISS.2018.8593231\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the popularity of social networking, consumer often relies on social network when making purchase decisions. Despite the growing importance of social network in supporting online purchase behavior, there has been limited research focusing on the effect of social network on consumer collaborative purchase behavior. This paper proposes a framework for understanding consumer’s collaborative purchase behavior within online social groups. It is one of the first studies to our knowledge that explore the combined effect of social network structure and content on consumer’s collaborative purchase. In this study, we first extract and analyze adjacent matrix, which representing related social networks, from a large dataset. This is then combined with interaction-based content analysis to identify the structure of social network. After verifying the impact of network attributes and structure on consumer purchases, we conducted content analysis based on chat records and identified productrelated interaction structures and content composition information based on the language action perspective. Finally, we combine content analysis with network topology analysis to construct a weighted social network and calculate the impact of these social networks on consumer purchasing behavior. Our research proposes a research framework to effectively collect, extract and analyze the structure and content of social networks.\",\"PeriodicalId\":338998,\"journal\":{\"name\":\"2018 8th International Conference on Logistics, Informatics and Service Sciences (LISS)\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 8th International Conference on Logistics, Informatics and Service Sciences (LISS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/LISS.2018.8593231\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 8th International Conference on Logistics, Informatics and Service Sciences (LISS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/LISS.2018.8593231","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Impact of Social Network: Understand Consumer’s Collaborative Purchase Behavior
With the popularity of social networking, consumer often relies on social network when making purchase decisions. Despite the growing importance of social network in supporting online purchase behavior, there has been limited research focusing on the effect of social network on consumer collaborative purchase behavior. This paper proposes a framework for understanding consumer’s collaborative purchase behavior within online social groups. It is one of the first studies to our knowledge that explore the combined effect of social network structure and content on consumer’s collaborative purchase. In this study, we first extract and analyze adjacent matrix, which representing related social networks, from a large dataset. This is then combined with interaction-based content analysis to identify the structure of social network. After verifying the impact of network attributes and structure on consumer purchases, we conducted content analysis based on chat records and identified productrelated interaction structures and content composition information based on the language action perspective. Finally, we combine content analysis with network topology analysis to construct a weighted social network and calculate the impact of these social networks on consumer purchasing behavior. Our research proposes a research framework to effectively collect, extract and analyze the structure and content of social networks.