Since its introduction a few years ago, live streaming service such as Facebook Live has been used in various activities such as sharing personal experiences, knowledge, performance, and e-commerce. Today, Facebook
Live is increasingly used by the individual, small sellers in Asia as a new interactive selling channel to sell their products, ranging from clothing to electronics to jewelry. Unlike larger corporations that limit the use of Facebook Live for advertising purposes, small sellers use it in demonstrating how products are created and used, interactively answering customer questions, and organizing real-time promotion activities that entertain and encourage customers to buy immediately. With the real-time nature of live streaming, it can mitigate trust and transparency problems buyers are concerned with regarding social commerce. We study consumer motivations and examining the influence of live streaming value on consumer trust and engagement intention from the survey data rather than real data from Facebook.
Thailand has the world’s highest proportion of shoppers buying directly from social media and also top the world ranking for the proportion of live streaming domestic viewers so we extracted the data from Thai Facebook sellers selling fashion and beauty products, popular products sold online. Through an exhaustive data analysis of all content posted by 11 of Thailand’s foremost fashion retail and cosmetics direct sellers on
Facebook, totaling between 74,000 and 380,000 followers, we seek to analyze the impact of Facebook Live mainly in terms of video content.
We consider engagement for posts of a different nature (video, photos, statuses, and links). Engagement metrics consist of comments, shares, and reactions within which we distinguish traditional “likes” from the recently introduced reactions smileys (e.g. “love”, “wow”, “haha”, “sad” and “angry”) reflecting more varied sentiments. The variability of consumer engagement is analyzed through a Principal Component Analysis, highlighting the changes induced by the use of Facebook Live.
Aim: How do shoppers respond to sellers’ live videos as compared to other content types?
data = read.csv(“Live.csv”)
Data = data[,-c(6:11)]
Data$num_reactions = Data$num_reactions – Data$num_likes
Data.pca <- prcomp(Data[,c(2:5)], center = TRUE,scale. = TRUE)
ggbiplot(Data.pca,ellipse=TRUE, labels=rownames(Data), groups=Data[,1])