IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/111239.html
   My bibliography  Save this paper

The Destruction of Price-Representativeness

Author

Listed:
  • 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 111239, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:111239
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/111239/1/MPRA_paper_111239.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    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. 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.
    4. 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.
    5. Arieh Gavious & Ella Segev, 2017. "Price Discrimination Based on Buyers’ Purchase History," Dynamic Games and Applications, Springer, vol. 7(2), pages 229-265, June.
    6. 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.
    7. Giovanni Ferri & Angelo Leogrande, 2015. "Was the Crisis due to a shift from stakeholder to shareholder finance? Surveying the debate," Mo.Fi.R. Working Papers 108, Money and Finance Research group (Mo.Fi.R.) - Univ. Politecnica Marche - Dept. Economic and Social Sciences.
    8. 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.
    9. 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.
    10. 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.
    11. Dosi,Giovanni & Galambos,Louis & Gambardella,Alfonso & Orsanigo,Luigi (ed.), 2013. "The Third Industrial Revolution in Global Business," Cambridge Books, Cambridge University Press, number 9781107028616, September.
    12. Kline, Brendan, 2015. "Identification of complete information games," Journal of Econometrics, Elsevier, vol. 189(1), pages 117-131.
    13. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ghasemaghaei, Maryam & Calic, Goran, 2019. "Does big data enhance firm innovation competency? The mediating role of data-driven insights," Journal of Business Research, Elsevier, vol. 104(C), pages 69-84.
    2. de Camargo Fiorini, Paula & Roman Pais Seles, Bruno Michel & Chiappetta Jabbour, Charbel Jose & Barberio Mariano, Enzo & de Sousa Jabbour, Ana Beatriz Lopes, 2018. "Management theory and big data literature: From a review to a research agenda," International Journal of Information Management, Elsevier, vol. 43(C), pages 112-129.
    3. Sidney Anderson, 2024. "Expanding data literacy to include data preparation: building a sound marketing analytics foundation," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(2), pages 227-234, June.
    4. Brewis, Claire & Dibb, Sally & Meadows, Maureen, 2023. "Leveraging big data for strategic marketing: A dynamic capabilities model for incumbent firms," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    5. Jingmei Gao & Zahid Sarwar, 2024. "How do firms create business value and dynamic capabilities by leveraging big data analytics management capability?," Information Technology and Management, Springer, vol. 25(3), pages 283-304, September.
    6. Loutfi, Ahmad Amine, 2022. "A framework for evaluating the business deployability of digital footprint based models for consumer credit," Journal of Business Research, Elsevier, vol. 152(C), pages 473-486.
    7. Patrick Mikalef & Ilias O. Pappas & John Krogstie & Michail Giannakos, 2018. "Big data analytics capabilities: a systematic literature review and research agenda," Information Systems and e-Business Management, Springer, vol. 16(3), pages 547-578, August.
    8. Nguyen Anh Khoa Dam & Thang Le Dinh & William Menvielle, 2019. "A systematic literature review of big data adoption in internationalization," Journal of Marketing Analytics, Palgrave Macmillan, vol. 7(3), pages 182-195, September.
    9. Thi Mai Le & Shu-Yi Liaw, 2017. "Effects of Pros and Cons of Applying Big Data Analytics to Consumers’ Responses in an E-Commerce Context," Sustainability, MDPI, vol. 9(5), pages 1-19, May.
    10. Liedong, Tahiru Azaaviele & Rajwani, Tazeeb & Lawton, Thomas C., 2020. "Information and nonmarket strategy: Conceptualizing the interrelationship between big data and corporate political activity," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    11. Zhiting Song & Yanming Sun & Jiafu Wan & Lingli Huang & Jianhua Zhu, 2019. "Smart e-commerce systems: current status and research challenges," Electronic Markets, Springer;IIM University of St. Gallen, vol. 29(2), pages 221-238, June.
    12. Ciechanowski, Leon & Jemielniak, Dariusz & Gloor, Peter A., 2020. "TUTORIAL: AI research without coding: The art of fighting without fighting: Data science for qualitative researchers," Journal of Business Research, Elsevier, vol. 117(C), pages 322-330.
    13. Shet, Sateesh.V. & Poddar, Tanuj & Wamba Samuel, Fosso & Dwivedi, Yogesh K., 2021. "Examining the determinants of successful adoption of data analytics in human resource management – A framework for implications," Journal of Business Research, Elsevier, vol. 131(C), pages 311-326.
    14. Vanhala, Mika & Lu, Chien & Peltonen, Jaakko & Sundqvist, Sanna & Nummenmaa, Jyrki & Järvelin, Kalervo, 2020. "The usage of large data sets in online consumer behaviour: A bibliometric and computational text-mining–driven analysis of previous research," Journal of Business Research, Elsevier, vol. 106(C), pages 46-59.
    15. Blasco-Arcas, Lorena & Lee, Hsin-Hsuan Meg & Kastanakis, Minas N. & Alcañiz, Mariano & Reyes-Menendez, Ana, 2022. "The role of consumer data in marketing: A research agenda," Journal of Business Research, Elsevier, vol. 146(C), pages 436-452.
    16. Shaw, F. Atiyya & Wang, Xinyi & Mokhtarian, Patricia L. & Watkins, Kari E., 2021. "Supplementing transportation data sources with targeted marketing data: Applications, integration, and internal validation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 149(C), pages 150-169.
    17. J. Piet Hausberg & Kirsten Liere-Netheler & Sven Packmohr & Stefanie Pakura & Kristin Vogelsang, 2019. "Research streams on digital transformation from a holistic business perspective: a systematic literature review and citation network analysis," Journal of Business Economics, Springer, vol. 89(8), pages 931-963, December.
    18. Keshav Singh Rawat & Sandeep Kumar Sood, 2021. "Emerging trends and global scope of big data analytics: a scientometric analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(4), pages 1371-1396, August.
    19. Hossain, Md Afnan & Akter, Shahriar & Yanamandram, Venkata, 2020. "Revisiting customer analytics capability for data-driven retailing," Journal of Retailing and Consumer Services, Elsevier, vol. 56(C).
    20. Sheng, Jie & Amankwah-Amoah, Joseph & Wang, Xiaojun, 2017. "A multidisciplinary perspective of big data in management research," International Journal of Production Economics, Elsevier, vol. 191(C), pages 97-112.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:111239. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.