The spread of the CoVid 19 virus is increasing non-face-to-face demand and accelerating the arrival of the new era such as the Era of Untact (No contact) Economy. As the online shopping market uainf smartphones is growing rapidly, live commerce using ...
The spread of the CoVid 19 virus is increasing non-face-to-face demand and accelerating the arrival of the new era such as the Era of Untact (No contact) Economy. As the online shopping market uainf smartphones is growing rapidly, live commerce using mobile video content is drawing people's attention. Live commerce is a channel where products are being introduced and sold through real-time online broadcasting. Consumers can get product information while watching online broadcasts, communicate in real-time with the facilitator or other viewers through chatting, and enjoy shopping at any time no matter where they are, as if they were physically at the store at that time. Live commerce is emerging as a next-generation shopping channel by supplementing the weaknesses of existing mobile shopping channels. Therefore, domestic e-commerce companies are making various attempts to secure their influential forces in the live commerce market.
This study intends to propose a recommendation service that will enhance the interactivity of live commerce as a way to strengthen competitiveness by increasing customers' intention to continue using the service in the fast-growing live commerce market. To this end, first, I have reviewed literature related to the hedonistic value of live commerce, shopping behaviors of female consumers, and the recommendation service. Next, as detailed components of the hedonistic value of live commerce, I have investigated the factors of achievement such as attractiveness of the host, communication with the host, communication with viewers, and derived the perception of new facts. And then, I conducted in-depth interviews with female consumers in their 20s and 30s who had experience using live commerce, and induced the problems and improvement points of the existing live commerce. Based on the in-depth interview results, I could specify three detailed components of interactivity such as the attractiveness of the host, communication with the host, and communication with the viewers and embody design ideas based on three detailed components. Then I verified three recommended service ideas through women in their 20s and 30s who have experience using live commerce. Finally, I produced the final design by reflecting the improvements. After establishing hypotheses regarding the effect of live commerce recommendation service on viewer attitudes and intention to use and conducting an online quantitative survey, I verified the hypotheses through regression analysis and analysis of variance (One-way-ANOVA).
In the study, it was found that the recommendation service based on the attractiveness of the host, communication with the host, and communication with the viewers affect the behavioral factors of viewers' attitudes, which in turn affect the intention to continue using it. Since all three types of recommendation service designs with enhanced interactivity can obtain desired information only when viewers take an active action, it is presumed that behavioral factors have a positive effect on continued service use. However, it was found that the recommendation service based on communication with viewers affects the cognitive factors of viewers' attitudes, which also affects the intention to continue using them. While the communication-based recommendation service with viewers is familiar because it is similar to the function of the existing SNS, the function to request a purchase through agent is expected to remain as an interesting and impressive service as a new function. The recommendation service based on communication with viewers seems to have influenced the cognitive factors because it consists of the newest function among the three recommendation service designs. In all three types of recommendation service designs with enhanced interactivity, the behavioral factors of viewers' attitudes had a significant effect on continued use intention. As users actively participate in the service (check, share, search, etc.), the users' benefits such as desired information and services get greater. Therefore, it is presumed that these behavioral factors had a positive effect on continued use of the service.