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The Destruction of Price-Representativeness

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  • LEOGRANDE, ANGELO

Abstract

The development of industry 4.0 and e-commerce destroy the traditional mechanism of price determination, the rigidity of supply in the short run and the idea of price representativeness. Industry 4.0 has changed the traditional view of price formation. Firms know the individual purchasing history of customers. Firms can extract the reserve price for each individual due to big data. Price is no more the encounter of supply and demand, but it is determinated considering the maximum amount that individuals can pay. The combination of data, dynamic pricing and price discrimination has destroyed one of the pillars of the mainstream economics: price representativeness. Dynamic pricing is the ability to change prices. Price discrimination is the ability to apply different prices for different customers for the same product or service.

Suggested Citation

  • Leogrande, Angelo, 2021. "The Destruction of Price-Representativeness," MPRA Paper 111224, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:111224
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    1. Yanzhe (Murray) Lei & Stefanus Jasin & Amitabh Sinha, 2018. "Joint Dynamic Pricing and Order Fulfillment for E-commerce Retailers," Manufacturing & Service Operations Management, INFORMS, vol. 20(2), pages 269-284, May.
    2. Jean Tirole, 2015. "Market Failures and Public Policy," American Economic Review, American Economic Association, vol. 105(6), pages 1665-1682, June.
    3. Shahriar Akter & Samuel Fosso Wamba, 2016. "Big data analytics in E-commerce: a systematic review and agenda for future research," Electronic Markets, Springer;IIM University of St. Gallen, vol. 26(2), pages 173-194, May.
    4. Sivarajah, Uthayasankar & Kamal, Muhammad Mustafa & Irani, Zahir & Weerakkody, Vishanth, 2017. "Critical analysis of Big Data challenges and analytical methods," Journal of Business Research, Elsevier, vol. 70(C), pages 263-286.
    5. Erevelles, Sunil & Fukawa, Nobuyuki & Swayne, Linda, 2016. "Big Data consumer analytics and the transformation of marketing," Journal of Business Research, Elsevier, vol. 69(2), pages 897-904.
    6. Eugene F. Fama, 1972. "Perfect Competition and Optimal Production Decisions under Uncertainty," Bell Journal of Economics, The RAND Corporation, vol. 3(2), pages 509-530, Autumn.
    7. Dosi,Giovanni & Galambos,Louis & Gambardella,Alfonso & Orsanigo,Luigi (ed.), 2013. "The Third Industrial Revolution in Global Business," Cambridge Books, Cambridge University Press, number 9781107028616.
    8. Muhmmad Islam Mahmoud Nofal & Zawiyah Mohammad Yusof, 2016. "Conceptual model of enterprise resource planning and business intelligence systems usage," International Journal of Business Information Systems, Inderscience Enterprises Ltd, vol. 21(2), pages 178-194.
    9. Giovanni Ferri & Angelo Leogrande, 2015. "Was the Crisis Due to a Shift from Stakeholder to Shareholder Finance? Surveying the Debate," Euricse Working Papers 1576, Euricse (European Research Institute on Cooperative and Social Enterprises).
    10. Kline, Brendan, 2015. "Identification of complete information games," Journal of Econometrics, Elsevier, vol. 189(1), pages 117-131.
    11. Chad Laux & Na Li & Corey Seliger & John Springer, 2017. "Impacting Big Data analytics in higher education through Six Sigma techniques," International Journal of Productivity and Performance Management, Emerald Group Publishing Limited, vol. 66(5), pages 662-679, June.
    12. Arieh Gavious & Ella Segev, 2017. "Price Discrimination Based on Buyers’ Purchase History," Dynamic Games and Applications, Springer, vol. 7(2), pages 229-265, June.
    13. Gyusun Hwang & Jeongcheol Lee & Jinwoo Park & Tai-Woo Chang, 2017. "Developing performance measurement system for Internet of Things and smart factory environment," International Journal of Production Research, Taylor & Francis Journals, vol. 55(9), pages 2590-2602, May.
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    More about this item

    Keywords

    Industry 4.0; Customer Behavior; Supply; Demand; Digital Economy; Nudge.;
    All these keywords.

    JEL classification:

    • E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E64 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Incomes Policy; Price Policy
    • E69 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Other

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