Mining marketing knowledge to explore social network sites and online purchase behaviors

[[abstract]]Social network sites (SNS), as web-based services, allow users to make open or semiopen profiles within the systems they are part of, to see lists of other people in the group, and to see the relationships of people within different groups. As the development of Internet applications has matured, developing and evaluating business models on social network sites has become a critical issue because these sites can be an innovative source for online marketing. Most studies in Taiwan on the behavior or marketing on SNS focus on either advertising or marketing, without picturing the overall scenario. Thus, this study investigates SNS as a research subject, and explores users’ online and purchase behaviors in the cybercommunity. For this, the study uses the Apriori algorithm as an association rules approach, and cluster analysis for data mining, to categorize four kinds of online user behavior and generate purchase behavior patterns and rules. The results suggest that online users’ SNS and purchase behavior knowledge are critical for the development of online business models.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]紙

Mining User Knowledge for Investigating the Facebook Business Model: The Case of Taiwan Users

[[abstract]]Social network sites (SNS), as web-based services, allow users to make open or semi-open profiles within the systems they are part of, to see lists of other people in the group and to see the relations of people within different groups. Facebook is essentially an online social network site in which individuals can share photographs, personal information, and join groups of friends. This study investigates the experiences on Facebook of various users in Taiwan. Their degrees of confidence were often demonstrated by word-of-mouth disseminations about the social network site. Further, this research looks at how the reputations of Facebook proprietors and their affiliates were disseminated through relationship marketing for formulated social network marketing in its business model concerns. Therefore, this study uses the a priori algorithm as an association rules approach, and cluster analysis for data mining. We divide Facebook users into two groups of contributors and lurkers by their profiles and then find each group’s social network community information utilization and online purchase behaviors for investigating the Facebook business models.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]紙

Digital Food Marketing to Children and Adolescents: Problematic Practices and Policy Interventions

Examines trends in digital marketing to youth that uses "immersive" techniques, social media, behavioral profiling, location targeting and mobile marketing, and neuroscience methods. Recommends principles for regulating inappropriate advertising to youth

Conceptualizing the Electronic Word-of-Mouth Process: What We Know and Need to Know About eWOM Creation, Exposure, and Evaluation

Electronic word of mouth (eWOM) is a prevalent consumer practice that has undeniable effects on the company bottom line, yet it remains an over-labeled and under-theorized concept. Thus, marketers could benefit from a practical, science-based roadmap to maximize its business value. Building on the consumer motivation–opportunity–ability framework, this study conceptualizes three distinct stages in the eWOM process: eWOM creation, eWOM exposure, and eWOM evaluation. For each stage, we adopt a dual lens—from the perspective of the consumer (who sends and receives eWOM) and that of the marketer (who amplifies and manages eWOM for business results)—to synthesize key research insights and propose a research agenda based on a multidisciplinary systematic review of 1050 academic publications on eWOM published between 1996 and 2019. We conclude with a discussion of the future of eWOM research and practice

Customer purchase behavior prediction in E-commerce: a conceptual framework and research agenda

Digital retailers are experiencing an increasing number of transactions coming from their consumers online, a consequence of the convenience in buying goods via E-commerce platforms. Such interactions compose complex behavioral patterns which can be analyzed through predictive analytics to enable businesses to understand consumer needs. In this abundance of big data and possible tools to analyze them, a systematic review of the literature is missing. Therefore, this paper presents a systematic literature review of recent research dealing with customer purchase prediction in the E-commerce context. The main contributions are a novel analytical framework and a research agenda in the field. The framework reveals three main tasks in this review, namely, the prediction of customer intents, buying sessions, and purchase decisions. Those are followed by their employed predictive methodologies and are analyzed from three perspectives. Finally, the research agenda provides major existing issues for further research in the field of purchase behavior prediction online

PROFILING SOCIAL MEDIA TOURISTS USING LITERATURE DURING 2015-2019: CRIMINAL PROFILING METHOD

With the continuous development of mobile commerce and the Internet, social media has deeply penetrated people’s lives and fundamentally changed the way of searching, reading and using travel-related information. With this backdrop, this research studied social media tourists (SMTs) who share or acquire information related to the hospitality and tourism on social media platforms. Based on 271 empirical articles retrieved from major databases and top hospitality and tourism journals in the recent five years from 2015 to 2019, this research developed a profiling framework about SMTs using criminal profiling method. The findings showed the possibility of using the criminal profiling method to analyze SMTs and provided a holistic personal, social-psychological, and behavioral profile of SMTs. Theoretical and practical implications were discussed