Author List: Luo, Xueming; Zhang, Jie (Jennifer);
Journal of Management Information Systems, 2013, Volume 30, Issue 2, Page 213-238.
Consumer buzz in the form of user-generated reviews, recommendations, and blogs signals that consumer attitude and advocacy can influence firm value. Web traffic also affects brand awareness and customer acquisition, and is a predictor of the performance of a firm's stock in the market. The information systems and accounting literature have treated buzz and traffic separately in studying their relationships with firm performance. We consider the interactions between buzz and traffic as well as competitive effects that have been overlooked heretofore. To study the relationship between user-initiated Web activities and firm performance, we collected a unique data set with metrics for consumer buzz, Web traffic, and firm value. We employed a vector autoregression with exogenous variables model that captures the evolution and interdependence between the time series of dependent variables. This model enables us to examine a series of questions that have been raised but not fully explored to date, such as dynamic effects, interaction effects, and market competition effects. Our results support the dynamic relationships of buzz and traffic with firm value as well as the related mediation effects of buzz and traffic. They also reveal significant market competition effects, including effects of both a firm's own and its rivals' buzz and traffic. The findings also provide insights for e-commerce managers regarding Web site design, customer relation management, and how to best respond to competitors' strategic moves.
Keywords: consumer buzz; firm value; online reviews; social media; stock market performance; vector autoregression; Web traffic
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#114 0.344 performance firm measures metrics value relationship firms results objective relationships firm's organizational traffic measure market study improve accounting measuring aggregate
#285 0.167 effects effect research data studies empirical information literature different interaction analysis implications findings results important set large provide using paper
#33 0.064 web site sites content usability page status pages metrics browsing design use web-based guidelines results implications portal loyalty navigability addition
#173 0.063 effect impact affect results positive effects direct findings influence important positively model data suggest test factors negative affects significant relationship
#131 0.060 media social content user-generated ugc blogs study online traditional popularity suggest different discourse news making anonymity marketing videos choices page
#242 0.057 market competition competitive network markets firms products competing competitor differentiation advantage competitors presence dominant structure share using incumbent make important
#199 0.051 reviews product online review products wom consumers consumer ratings sales word-of-mouth impact reviewers word using effect marketing helpfulness electronic commerce