Time:2019.06.05, 9:30-11:00 AM

Location:Room 615, Main Building

KeynoteSpeaker:Dr.Liu Lei, Associate Professor, School of Culture and Communications, CUFE

Abstract:Due to therelentless pace of developments in social media and information technology,firms increasingly rely on a combination of verbal and visual elements tocommunicate with consumers. The present research investigates the impacts oftext-image information on customer engagement and corporate value. Based on alarge scale data on Sina Weibo, we employ a Natural Language Processingalgorithm to characterize text contents as function-oriented (information thatis helpful for increasing consumer knowledge about the product, brand, orcompany) and social-bond oriented (i.e., information conveyed on a sociallevel, to create connections with customers) types. Further, using DeepLearning techniques, we respectively calculate each message’s relevancy andexpectancy levels between text and images, which are the two dimensions of incongruence.The results indicate that compared to function-oriented text, socialbond-oriented text that aims to connect with consumers emotionally exerts alarger effect on firm value measured by abnormal returns (AR) through themediating role of the number of likes. More interestingly, we find thatforward, comment and“Like”are virtually different in antecedentfactors and influence. Specifically, there exist inverted-relationships betweenexpectancy and the number of forwards, the number of comments as well as thenumber of likes. But expectancy doesn’t exert a further effect on firm value.In contrast, there is a U-relationship between relevancy and the number oflikes, which further drives AR.