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 |