An Explorative Study on Sales Distribution in M-commerce

Despite the proliferation of studies on the sales distribution in e-commerce, little research has been conducted on the sales distribution in the m-commerce channel. This study empirically examines the sales distribution of various product categories in the mobile channel, using the large transaction data from a leading e-marketplace in Korea. Overall, transactions in the mobile channel are more concentrated to head products compared to the PC channel sales, but the pattern is inconsistent across product categories. Transactions in product categories of high average price (e.g., computers) and low purchase frequency rate (e.g., health care products) are less concentrated to head products in the mobile channel than the PC channel. The revenue distribution, however, shows the opposite. Head products generate relatively less revenue in the mobile channel than the PC channel. We provide explanations why the mixing results appear across product categories and between the distribution types.

[1]  Arun Sundararajan,et al.  Recommendation Networks and the Long Tail of Electronic Commerce , 2010, MIS Q..

[2]  Oliver Hinz,et al.  Drivers of the Long Tail Phenomenon: An Empirical Analysis , 2011, J. Manag. Inf. Syst..

[3]  J. Avery,et al.  The long tail. , 1995, Journal of the Tennessee Medical Association.

[4]  Wenqi Zhou,et al.  Online User Reviews, Product Variety, and the Long Tail: An Empirical Investigation on Online Software Downloads , 2010, Electron. Commer. Res. Appl..

[5]  L. Krishnamurthi,et al.  On the Go: How Mobile Shopping Affects Customer Purchase Behavior , 2015 .

[6]  G. Laurent,et al.  Measuring Consumer Involvement Profiles , 1985 .

[7]  Jae-Hyeon Ahn,et al.  Channel Capabilities, Product Characteristics, and Impacts of Mobile Channel Introduction , 2013 .

[8]  Alain Pinsonneault,et al.  Free Versus For-a-Fee: The Impact of a Paywall on the Pattern and Effectiveness of Word-of-Mouth via Social Media , 2016, MIS Q..

[9]  Sung-Hyuk Park,et al.  The Negative Impact of Mobile Devices on Niche Product Consumption , 2013, ICIS.

[10]  Anita Elberse,et al.  Superstars and Underdogs: An Examination of the Long Tail Phenomenon in Video Sales , 2007 .

[11]  François Moreau,et al.  Internet and the ‘Long Tail versus superstar effect’ debate: evidence from the French book market , 2012 .

[12]  Avi Goldfarb,et al.  How Is the Mobile Internet Different? Search Costs and Local Activities , 2013, Inf. Syst. Res..

[13]  G. Laurent,et al.  Consumers' Involvement Profile: New Empirical Results , 1985 .

[14]  Erik Brynjolfsson,et al.  Goodbye Pareto Principle, Hello Long Tail: The Effect of Search Costs on the Concentration of Product Sales , 2011, Manag. Sci..

[15]  Jaemin Jung,et al.  Measuring usage concentration of smartphone applications: Selective repertoire in a marketplace of choices , 2014 .

[16]  Florian Michahelles,et al.  Google play is not a long tail market: an empirical analysis of app adoption on the Google play app market , 2013, SAC '13.

[17]  Erik Brynjolfsson,et al.  Consumer Surplus in the Digital Economy: Estimating the Value of Increased Product Variety at Online Booksellers , 2003, Manag. Sci..

[18]  Avi Goldfarb,et al.  How is the Mobile Internet Different? , 2012, ICIS.

[19]  Yu Jeffrey Hu,et al.  Consumer Surplus in the Digital Economy: Estimating the Value of Increased Product Variety at Online Booksellers , 2003 .

[20]  Rahul Telang,et al.  Information Discovery and the Long Tail of Motion Picture Content , 2014, MIS Q..

[21]  Erik Brynjolfsson,et al.  The Longer Tail: The Changing Shape of Amazon’s Sales Distribution Curve , 2010 .

[22]  B. Ratchford.  Cost-Benefit Models for Explaining Consumer Choice and Information Seeking Behavior , 1982 .

[23]  J. Bakos.  Reducing buyer search costs: implications for electronic marketplaces , 1997 .

[24]  Arjun Chaudhuri,et al.  A Macro Analysis of the Relationship of Product Involvement and Information Search: The Role of Risk , 2000 .

[25]  Serguei Netessine,et al.  Is Tom Cruise Threatened ? Using Netflix Prize Data to Examine the Long Tail of Electronic Commerce , 2009 .

[26]  Peter S. Fader,et al.  RFM and CLV: Using Iso-Value Curves for Customer Base Analysis , 2005 .