Measuring the Business Value of Online Social Media Content for Marketers

Marketers have been rushing to increase their social media marketing expenditures. However, the state of empirical research, business intelligence and analytics into the business value of social media marketing engagements have still lagged behind. Prior research about the impact of social media user&gene rated content on aggregate sales has overlooked the qualitative aspects. Moreover, due to the co&existence of consumers and marketers in social media, how these two roles generate business value has been understudied. Therefore, this study proposes the concept of social media marketer& generated content, and investigates the business value of user& and marketer&generated content, focusing on content sentiment and information. Ordinary least squares specification is employed to model sales performance. We find that the qualitative nature of social media content indeed has some business value. Specifically, we find a significant relationship between the richness of information embedded in both user& and marketer&generated content and firm sales performan ce.

[1]  Anindya Ghose,et al.  Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets , 2008, Inf. Syst. Res..

[2]  Daniel A. Ackerberg Empirically Distinguishing Informative and Prestige Effects of Advertising , 2001 .

[3]  X. Zhang,et al.  Impact of Online Consumer Reviews on Sales: The Moderating Role of Product and Consumer Characteristics , 2010 .

[4]  Pei-Yu Sharon Chen,et al.  The Impact of Online Recommendations and Consumer Feedback on Sales , 2004, ICIS.

[5]  Yong Liu Word-of-Mouth for Movies: Its Dynamics and Impact on Box Office Revenue , 2006 .

[6]  Markus Christen,et al.  Using Market-Level Data to Understand Promotion Effects in a Nonlinear Model , 1997 .

[7]  David Godes,et al.  Using Online Conversations to Study Word-of-Mouth Communication , 2004 .

[8]  Pradeep K. Chintagunta,et al.  The Effect of Banner Advertising on Internet Purchasing , 2006 .

[9]  E. Clemons,et al.  When Online Reviews Meet Hyperdifferentiation: A Study of the Craft Beer Industry , 2006 .

[10]  M. Wedel,et al.  The Effectiveness of Customized Promotions in Online and Offline Stores , 2009 .

[11]  Pradeep K. Chintagunta,et al.  The role of self selection, usage uncertainty and learning in the demand for local telephone service , 2007 .

[12]  P. Nelson Information and Consumer Behavior , 1970, Journal of Political Economy.

[13]  A. Tversky,et al.  Prospect theory: an analysis of decision under risk — Source link , 2007 .

[14]  H. Theil Principles of econometrics , 1971 .

[15]  B. Gu,et al.  The impact of online user reviews on hotel room sales , 2009 .

[16]  D. Iacobucci,et al.  Dynamic Effects among Movie Ratings, Movie Revenues, and Viewer Satisfaction , 2010 .

[17]  Eric K. Clemons,et al.  When Online Reviews Meet Hyperdifferentiation: A Study of Craft Beer Industry , 2006, Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06).

[18]  John D. Hey,et al.  Consumer Search with Uncertain Product Quality , 1981, Journal of Political Economy.

[19]  Frank M. Bass,et al.  A New Product Growth for Model Consumer Durables , 2004, Manag. Sci..

[20]  A. Tversky,et al.  Prospect Theory : An Analysis of Decision under Risk Author ( s ) : , 2007 .

[21]  Yubo Chen,et al.  Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix , 2004, Manag. Sci..

[22]  J. Edward Russo,et al.  How persuasive messages can influence behavior without awareness , 2010 .

[23]  Channel Is Persuasive Advertising Always Combative in a Distribution Channel ? , 2009 .

[24]  Xiaoquan Zhang,et al.  AIS Electronic Library (AISeL) , 2017 .

[25]  Bin Gu,et al.  Do online reviews matter? - An empirical investigation of panel data , 2008, Decis. Support Syst..

[26]  Joan Meyers-Levy,et al.  Consumers' processing of persuasive advertisements: An integrative framework of persuasion theories , 1999 .