Information effect in Social Commerce: A case of TicketMonster

Social Commerce sites are in vogue enough to be recognized as a new trend in online shopping arena. Social Commerce can be defined as the electronic commerce triggered by social media. It has been growing very rapidly with enormous discount rate, quality services and precise information. This research analyzes effects of posted numeric information on daily sales volumes. Hetrosckedasticity arises with the real transaction data that was acquired from TicketMonster which is one of the biggest Social Commerce sites in Korea. Therefore, GLS model was applied to have results that original price, discounted price, minimum quantity to have discounted price, and maximum units of sales are statistically significant. Minimum quantity of sales to meet the requirement to have discounted prices has threshold effect on the purchase of consumers like the ways they have group buying on the Internet. However, additional studies are required to identify if this correlated information can be results of reasonable estimates by the vendors and the intermediary or play a role of signal to attract sales. More research opportunities are addressed on services types, consumer groups and information richness.

[1]  Krishnan S. Anand,et al.  Group Buying on the Web: A Comparison of Price-Discovery Mechanisms , 2003, Manag. Sci..

[2]  R. MacAvoy,et al.  Frictionless Commerce? A Comparison of Internet and Conventional Retailers , 1999 .

[3]  Venkatesh Shankar,et al.  Price levels and price dispersion within and across multiple retailer types: Further evidence and extension , 2004 .

[4]  Bin Wang,et al.  Effects of daily and "woot-off" strategies on e-commerce , 2009, Ind. Manag. Data Syst..

[5]  Olivier Toubia,et al.  Deriving Value from Social Commerce Networks , 2009 .

[6]  Michael R. Baye,et al.  Price Dispersion in the Small and in the Large: Evidence from an Internet Price Comparison Site , 2004 .

[7]  Jian Chen,et al.  Bidder's strategy under group-buying auction on the Internet , 2002, IEEE Trans. Syst. Man Cybern. Part A.

[8]  Jian Chen,et al.  Comparison of the group-buying auction and the fixed pricing mechanism , 2007, Decis. Support Syst..

[9]  Jae-Cheol Kim,et al.  Pricing strategies in B2C electronic commerce: analytical and empirical approaches , 2005, Decis. Support Syst..

[10]  A. Roth,et al.  The Deadline Effect in Bargaining: Some Experimental Evidence , 1988 .

[11]  U. Dholakia How Effective are Groupon Promotions for Businesses? , 2010 .

[12]  H. White A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity , 1980 .

[13]  Bin Wang,et al.  New buyers' arrival under dynamic pricing market microstructure: the case of group-buying discounts on the Internet , 2001, Proceedings of the 34th Annual Hawaii International Conference on System Sciences.

[14]  A. Roth,et al.  Last-Minute Bidding and the Rules for Ending Second-Price Auctions: Evidence from eBay and Amazon Auctions on the Internet , 2002 .