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Database Submission ---Market Dynamics and User-Generated Content About Tablet Computers

Author

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  • Xin (Shane) Wang

    (Department of Marketing, Carl H. Lindner College of Business, University of Cincinnati, Cincinnati, Ohio 45221)

  • Feng Mai

    (Department of Operations, Business Analytics, and Information Systems, Carl H. Lindner College of Business, University of Cincinnati, Cincinnati, Ohio 45221)

  • Roger H. L. Chiang

    (Department of Operations, Business Analytics, and Information Systems, Carl H. Lindner College of Business, University of Cincinnati, Cincinnati, Ohio 45221)

Abstract

Our Tablet Computer data set, collected from various websites, contains market dynamics related to 2,163 products, characteristics of 794 products, more than 40,000 consumer-generated product reviews, and information about 39,278 reviewers. The market dynamic information was collected weekly for 24 weeks starting February 1, 2012. Our Tablet Computer data set comprises four tables: the Market Dynamics of Products, Product Characteristic Information, Consumer-Generated Product Reviews, and Reviewer Information tables. In turn, it offers three unique properties. First, it contains both structured product information and unstructured product reviews. Second, it comprises product characteristic information and market dynamic information. Third, this data set integrates user-generated content with manufacturer-provided content. This integrated data set (available at http://pubsonline.informs.org/page/mksc/online-databases ) is valuable for both academics and practitioners who conduct research related to marketing, information systems, computer science, and other fields using digital data readily available through the Internet.

Suggested Citation

  • Xin (Shane) Wang & Feng Mai & Roger H. L. Chiang, 2014. "Database Submission ---Market Dynamics and User-Generated Content About Tablet Computers," Marketing Science, INFORMS, vol. 33(3), pages 449-458, May.
    • Handle: RePEc:inm:ormksc:v:33:y:2014:i:3:p:449-458
    DOI: 10.1287/mksc.2013.0821
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    1. Michael R. Baye & John Morgan & Patrick Scholten, 2004. "Price Dispersion In The Small And In The Large: Evidence From An Internet Price Comparison Site," Journal of Industrial Economics, Wiley Blackwell, vol. 52(4), pages 463-496, December.
    2. Deneckere, Raymond & Marvel, Howard P & Peck, James, 1997. "Demand Uncertainty and Price Maintenance: Markdowns as Destructive Competition," American Economic Review, American Economic Association, vol. 87(4), pages 619-641, September.
    3. Anindya Ghose & Yuliang Yao, 2011. "Using Transaction Prices to Re-Examine Price Dispersion in Electronic Markets," Information Systems Research, INFORMS, vol. 22(2), pages 269-288, June.
    4. Anindya Ghose & Michael D. Smith & Rahul Telang, 2006. "Internet Exchanges for Used Books: An Empirical Analysis of Product Cannibalization and Welfare Impact," Information Systems Research, INFORMS, vol. 17(1), pages 3-19, March.
    5. Onishi, Hiroshi & Manchanda, Puneet, 2012. "Marketing activity, blogging and sales," International Journal of Research in Marketing, Elsevier, vol. 29(3), pages 221-234.
    6. Eric K. Clemons & Il-Horn Hann & Lorin M. Hitt, 2002. "Price Dispersion and Differentiation in Online Travel: An Empirical Investigation," Management Science, INFORMS, vol. 48(4), pages 534-549, April.
    7. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2011. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Management Science, INFORMS, vol. 57(8), pages 1485-1509, August.
    8. Peter S. Fader & Russell S. Winer, 2012. "Introduction to the Special Issue on the Emergence and Impact of User-Generated Content," Marketing Science, INFORMS, vol. 31(3), pages 369-371, May.
    9. Sanjiv R. Das & Mike Y. Chen, 2007. "Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web," Management Science, INFORMS, vol. 53(9), pages 1375-1388, September.
    10. Erik Brynjolfsson & Michael D. Smith, 2000. "Frictionless Commerce? A Comparison of Internet and Conventional Retailers," Management Science, INFORMS, vol. 46(4), pages 563-585, April.
    11. Chris Forman & Anindya Ghose & Batia Wiesenfeld, 2008. "Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets," Information Systems Research, INFORMS, vol. 19(3), pages 291-313, September.
    12. Michael R. Baye & John Morgan & Patrick Scholten, 2006. "Persistent Price Dispersion in Online Markets," Chapters, in: Dennis W. Jansen (ed.), The New Economy and Beyond, chapter 6, Edward Elgar Publishing.
    13. Anindya Ghose & Panagiotis G. Ipeirotis & Beibei Li, 2012. "Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowdsourced Content," Marketing Science, INFORMS, vol. 31(3), pages 493-520, May.
    14. Anindya Ghose & Tridas Mukhopadhyay & Uday Rajan, 2007. "The Impact of Internet Referral Services on a Supply Chain," Information Systems Research, INFORMS, vol. 18(3), pages 300-319, September.
    15. Oded Netzer & Ronen Feldman & Jacob Goldenberg & Moshe Fresko, 2012. "Mine Your Own Business: Market-Structure Surveillance Through Text Mining," Marketing Science, INFORMS, vol. 31(3), pages 521-543, May.
    16. Carl F. Mela, 2011. "Structural Workshop Paper --Data Selection and Procurement," Marketing Science, INFORMS, vol. 30(6), pages 965-976, November.
    17. J. Yannis Bakos, 1997. "Reducing Buyer Search Costs: Implications for Electronic Marketplaces," Management Science, INFORMS, vol. 43(12), pages 1676-1692, December.
    18. Seshadri Tirunillai & Gerard J. Tellis, 2012. "Does Chatter Really Matter? Dynamics of User-Generated Content and Stock Performance," Marketing Science, INFORMS, vol. 31(2), pages 198-215, March.
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    6. Saeed Tajdini, 2023. "The effects of internet search intensity for products on companies’ stock returns: a competitive intelligence perspective," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(3), pages 352-365, September.

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