What matters customer engagement in live streaming e-commerce platform? (The impact of celebrity live streamer endorser effectiveness and social support)

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dc.contributor.advisor Hinterhuber, Andreas it_IT
dc.contributor.author Hu, Juanli <1988> it_IT
dc.date.accessioned 2021-10-04 it_IT
dc.date.accessioned 2022-01-11T09:27:30Z
dc.date.available 2022-01-11T09:27:30Z
dc.date.issued 2021-10-28 it_IT
dc.identifier.uri http://hdl.handle.net/10579/20444
dc.description.abstract With the popularity and growth of live streaming e-commerce, customers in China rely heavily on suggestions and recommendations from live streamers and peers in the live streaming e-commerce community when making purchase decisions. Enhancing customer engagement in the context of live streaming e-commerce is critical for operators looking to maximize traffic monetization. Applying the social support theory and Source model from endorser theory, I develop theoretical model to understand how live steamers and the community effect on customer engagement, which is tested by using 100 valid responses from customers in Taobao Live. The results show that credibility of live streamer (expertise, trustworthiness and attractiveness) and attractiveness of live streamer (familiarity, likeability, and similarity) had positively significant relationships with social support (emotional support and informational support) and customer engagement. Specially, the unexpected result that emotional support has negative effect on customer engagement provides a new perspective about community in live streaming context is totally different from s-commerce. It emphasises the significance of comprehending the implications of community formation from various perspectives in live streaming e-commerce. it_IT
dc.language.iso en it_IT
dc.publisher Università Ca' Foscari Venezia it_IT
dc.rights © Juanli Hu, 2021 it_IT
dc.title What matters customer engagement in live streaming e-commerce platform? (The impact of celebrity live streamer endorser effectiveness and social support) it_IT
dc.title.alternative What matters customer engagement in live streaming e-commerce platform? (The impact of celebrity live streamer endorser effectiveness and social support) it_IT
dc.type Master's Degree Thesis it_IT
dc.degree.name Management it_IT
dc.degree.level Laurea magistrale it_IT
dc.degree.grantor Dipartimento di Management it_IT
dc.description.academicyear 2020/2021_sessione autunnale_181021 it_IT
dc.rights.accessrights openAccess it_IT
dc.thesis.matricno 882331 it_IT
dc.subject.miur SECS-P/07 ECONOMIA AZIENDALE it_IT
dc.description.note With the popularity and growth of live streaming e-commerce, customers in China rely heavily on suggestions and recommendations from live streamers and peers in the live streaming e-commerce community when making purchase decisions. Enhancing customer engagement in the context of live streaming e-commerce is critical for operators looking to maximize traffic monetization. Applying the social support theory and Source model for celebrity endorsement, I develop theoretical model to value live steamers and the community effect on customer engagement, which is tested by using 100 valid responses from customers in Taobao Live. The results show that credibility of live streamer (expertise, trustworthiness and attractiveness) and attractiveness of live streamer (familiarity, likeability, and similarity) had positively significant relationships with social support (emotional support and informational support) and customer engagement. Specially, the unexpected result that emotional support has negative effect while informational support is positive effect on customer engagement provides a new perspective about community in live streaming e-commerce context. In this regard, it shows that live streaming e-commerce seems to totally differ from s-commerce, which is mainly used for shopping rather than social interaction as, instead, is for live streaming e-commerce. it_IT
dc.degree.discipline it_IT
dc.contributor.co-advisor it_IT
dc.date.embargoend it_IT
dc.provenance.upload Juanli Hu (882331@stud.unive.it), 2021-10-04 it_IT
dc.provenance.plagiarycheck Andreas Hinterhuber (andreas.hinterhuber@unive.it), 2021-10-18 it_IT


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