Adida, E., & Özer, Ö. (2019). Why markdown as a pricing modality? Management Science, 65(5), 2161–2178. https://doi.org/10.1287/mnsc.2018.3046.
Article
Google Scholar
Ajorlou, A., Jadbabaie, A., & Kakhbod, A. (2018). Dynamic pricing in social networks: The word-of-mouth effect. Management Science, 64(2), 971–979. https://doi.org/10.1287/mnsc.2016.2657.
Article
Google Scholar
Allender, W. J., Liaukonyte, J., & Richards, T. J. (2016). Strategic obfuscation and price fairness. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2780170.
Article
Google Scholar
Amaldoss, W., & He, C. (2019). The charm of behavior-based pricing: When consumers’ taste is diverse and the consideration set is limited. Journal of Marketing Research, 56(5), 767–790. https://doi.org/10.1177/0022243719834945.
Article
Google Scholar
Angel, J. J., & McCabe, D. M. (2018). Insider trading 2.0? The ethics of information sales. Journal of Business Ethics, 147(4), 747–760. https://doi.org/10.1007/s10551-016-3391-4.
Article
Google Scholar
Araman, V. F., & Caldentey, R. (2009). Dynamic pricing for nonperishable products with demand learning. Operations Research, 57(5), 1169–1188. https://doi.org/10.1287/opre.1090.0725.
Article
Google Scholar
Aviv, Y., & Vulcano, G. (2012). Dynamic list pricing. The Oxford handbook of pricing management. Oxford: Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199543175.013.0023.
Book
Google Scholar
Ayadi, N., Paraschiv, C., & Rousset, X. (2017). Online dynamic pricing and consumer-perceived ethicality: Synthesis and future research. Recherche et Applications en Marketing (English Edition), 32(3), 49–70. https://doi.org/10.1177/2051570717702592.
Article
Google Scholar
Aydin, G., & Ziya, S. (2009). Technical note: Personalized dynamic pricing of limited inventories. Operations Research, 57(6), 1523–1531. https://doi.org/10.1287/opre.1090.0701.
Article
Google Scholar
Balmaceda, F., & Soruco, P. (2008). Asymmetric dynamic pricing in a local gasoline retail market. Journal of Industrial Economics, 56(3), 629–653. https://doi.org/10.1111/j.1467-6451.2008.00349.x.
Article
Google Scholar
Bar-Gill, O. (2019). Algorithmic price discrimination when demand is a function of both preferences and (mis)perceptions. The University of Chicago Law Review, 86(2), 217–254. https://lawreview.uchicago.edu/publication/algorithmic-price-discrimination-when-demand-function-both-preferences-and.
Barone, M. J., & Roy, T. (2010). The effect of deal exclusivity on consumer response to targeted price promotions: A social identification perspective. Journal of Consumer Psychology, 20(1), 78–89. https://doi.org/10.1016/j.jcps.2009.10.002.
Article
Google Scholar
Belobaba, P. P. (1987a). Air travel demand and airline seat inventory management. Working Paper/Dissertation. Ph.D. dissertation. Cambridge, MA : Flight Transportation Laboratory, Massachusetts Institute of Technology. Retrieved from https://dspace.mit.edu/handle/1721.1/68077.
Belobaba, P. P. (1987b). Survey paper: Airline yield management an overview of seat inventory control. Transportation Science, 21(2), 63–73. https://doi.org/10.1287/trsc.21.2.63.
Article
Google Scholar
Belobaba, P. P. (1989). OR practice: Application of a probabilistic decision model to airline seat inventory control. Operations Research, 37(2), 183–197. https://doi.org/10.1287/opre.37.2.183.
Article
Google Scholar
Béranger, J. (2018). The algorithmic code of ethics: Ethics at bedside of digital revolution. London and Hoboken: Wiley.
Book
Google Scholar
Bergemann, D., & Välimäki, J. (2006). Dynamic pricing of new experience goods. Journal of Political Economy, 114(4), 713–743. https://doi.org/10.1086/506923.
Article
Google Scholar
Bergen, M., Ritson, M., Dutta, S., Levy, D., & Zbarachi, M. (2003). Shattering the myth of costless price changes. European Management Journal, 21(6), 663–669. https://doi.org/10.1016/j.emj.2003.09.018.
Article
Google Scholar
Besbes, O., & Sauré, D. (2014). Dynamic pricing strategies in the presence of demand shifts. Manufacturing & Service Operations Management, 16(4), 513–528. https://doi.org/10.1287/msom.2014.0489.
Article
Google Scholar
Besbes, O., & Zeevi, A. (2009). Dynamic pricing without knowing the demand function: Risk bounds and near-optimal algorithms. Operations Research, 57(6), 1407–1420. https://doi.org/10.1287/opre.1080.0640.
Article
Google Scholar
Besbes, O., & Zeevi, A. (2015). On the (surprising) sufficiency of linear models for dynamic pricing with demand learning. Management Science, 61(4), 723–739. https://doi.org/10.1287/mnsc.2014.2031.
Article
Google Scholar
Bitran, G., & Caldentey, R. (2003). An overview of pricing models for revenue management. Manufacturing & Service Operations Management, 5(3), 203–229. https://doi.org/10.1287/msom.5.3.203.16031.
Article
Google Scholar
Bitran, G. R., & Mondschein, S. V. (1997). Periodic pricing of seasonal products in retailing. Management Science, 43(1), 64–79. https://doi.org/10.1287/mnsc.43.1.64.
Article
Google Scholar
Bock, C. (2016). Preserve personal freedom in networked societies. Nature, 537(7618), 9. https://doi.org/10.1038/537009a.
Article
Google Scholar
Bolton, L. E., Keh, H. T., & Alba, J. W. (2010). How do price fairness perceptions differ across culture? Journal of Marketing Research, 47(3), 564–576. https://doi.org/10.1509/jmkr.47.3.564.
Article
Google Scholar
Bouchet, A., Troilo, M., & Walkup, B. R. (2016). Dynamic pricing usage in sports for revenue management. Managerial Finance, 42(9), 913–921. https://doi.org/10.1108/MF-01-2016-0017.
Article
Google Scholar
Bront, J. J. M., Méndez-Díaz, I., & Vulcano, G. (2009). A column generation algorithm for choice-based network revenue management. Operations Research, 57(3), 769–784. https://doi.org/10.1287/opre.1080.0567.
Article
Google Scholar
Brumelle, S. L., & McGill, J. I. (1993). Airline seat allocation with multiple nested fare classes. Operations Research, 41(1), 127–137. https://doi.org/10.1287/opre.41.1.127.
Article
Google Scholar
Buhmann, A., Paßmann, J., & Fieseler, C. (2019). Managing algorithmic accountability: Balancing reputational concerns, engagement strategies, and the potential of rational discourse. Journal of Business Ethics. https://doi.org/10.1007/s10551-019-04226-4.
Article
Google Scholar
Calvano, E., Calzolari, G., Denicolò, V., & Pastorello, S. (2019). Algorithmic pricing what implications for competition policy? Review of Industrial Organization, 55(1), 155–171. https://doi.org/10.1007/s11151-019-09689-3.
Article
Google Scholar
Caplan, B., & Stringham, E. P. (2008). Privatizing the adjudication of disputes. Theoretical Inquiries in Law, 9(2), 503–528. https://doi.org/10.2202/1565-3404.1195.
Article
Google Scholar
Chen, L. (2017). Measuring algorithms in online marketplaces. ProQuest Dissertations and Theses, 149. https://repository.library.northeastern.edu/files/neu:cj82q9886.
Chen, M., & Chen, Z. L. (2015). Recent developments in dynamic pricing research: Multiple products, competition, and limited demand information. Production and Operations Management, 24(5), 704–731. https://doi.org/10.1111/poms.12295.
Article
Google Scholar
Chen, N., & Gallego, G. (2019). Welfare analysis of dynamic pricing. Management Science, 65(1), 139–151. https://doi.org/10.1287/mnsc.2017.2943.
Article
Google Scholar
Chen, Y., & Iyer, G. (2001). Research note: Consumer addressability and customized pricing. Marketing Science, 21(2), 197–208. https://doi.org/10.1287/mksc.21.2.197.153.
Article
Google Scholar
Chen, Q. G., Jasin, S., & Duenyas, I. (2016a). Real-time dynamic pricing with minimal and flexible price adjustment. Management Science, 62(8), 2437–2455. https://doi.org/10.1287/mnsc.2015.2238.
Article
Google Scholar
Chen, L., Mislove, A., & Wilson, C. (2016). An empirical analysis of algorithmic pricing on amazon marketplace. In Proceedings of the 25th International Conference on World Wide Web—WWW’16 (pp. 1339–1349). New York: ACM Press. https://doi.org/10.1145/2872427.2883089.
Chen, M. K., & Sheldon, M. (2016). Dynamic pricing in a labor market: Surge pricing and flexible work on the uber platform. In Proceedings of the 2016 ACM conference on economics and computation. https://doi.org/10.1145/2940716.2940798.
Chen, Y., & Sudhir, K. (2004). When shopbots meet emails: Implications for price competition on the Internet. Quantitative Marketing and Economics, 2(3), 233–255. https://doi.org/10.2139/ssrn.291199.
Article
Google Scholar
Cho, M., Fan, M., & Zhou, Y.-P. (2009). Strategic consumer response to dynamic pricing of perishable products. In C. Tang & S. Netessine (Eds.), Consumer-driven demand and operations management models (pp. 435–458). Boston, MA: Springer. https://doi.org/10.1007/978-0-387-98026-3_17.
Chapter
Google Scholar
Choe, C., King, S., & Matsushima, N. (2018). Pricing with cookies: Behavior-based price discrimination and spatial competition. Management Science, 64(12), 5669–5687. https://doi.org/10.1287/mnsc.2017.2873.
Article
Google Scholar
Choe, P., & Wu, J. (2015). Customer perceptions toward dynamic pricing for wireless data service. International Journal of Mobile Communications, 13(2), 172. https://doi.org/10.1504/IJMC.2015.067962.
Article
Google Scholar
Choudhary, V., Ghose, A., Mukhopadhyay, T., & Rajan, U. (2005). Personalized pricing and quality differentiation. Management Science, 51(7), 1120–1130. https://doi.org/10.1287/mnsc.1050.0383.
Article
Google Scholar
Coffey, H. (2018). Airlines face crack down on use of ‘exploitative’ algorithm that splits up families on flights. The Independent. Retrieved July 26, 2019 from https://www.independent.co.uk/travel/news-and-advice/airline-flights-pay-extra-to-sit-together-split-up-family-algorithm-minister-a8640771.html.
Cohen, M. C. (2018). Big data and service operations. Production and Operations Management, 27(9), 1709–1723. https://doi.org/10.1111/poms.12832.
Article
Google Scholar
Cohen, M. C., Lobel, R., & Perakis, G. (2018). Dynamic pricing through data sampling. Production and Operations Management, 27(6), 1074–1088. https://doi.org/10.1111/poms.12854.
Article
Google Scholar
Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to algorithms (3rd ed.). Cambridge: MIT Press.
Google Scholar
Cosguner, K., Chan, T. Y., & Seetharaman, P. B. (2018). Dynamic pricing in a distribution channel in the presence of switching costs. Management Science, 64(3), 1212–1229. https://doi.org/10.1287/mnsc.2016.2649.
Article
Google Scholar
Cox, J. (2017). London terror attack: Uber slammed for being slow to turn off ‘surge pricing’ after rampage. The Independent. Retrieved April 28, 2018 from https://www.independent.co.uk/news/uk/home-news/london-terror-attack-uber-criticised-surge-pricing-after-london-bridge-black-cab-a7772246.html.
Dakers, M. (2016). Uber knows customers with dying batteries are more likely to accept surge pricing. The Telegraph. Retrieved October 30, 2017 from http://www.telegraph.co.uk/business/2016/05/22/uber-app-can-detect-when-a-users-phone-is-about-to-die/.
Danziger, S., Hadar, L., & Morwitz, V. G. (2014). Retailer pricing strategy and consumer choice under price uncertainty. Journal of Consumer Research, 41(3), 761–774. https://doi.org/10.1086/677313.
Article
Google Scholar
Das, T. K., Gosavi, A., Mahadevan, S., & Marchalleck, N. (1999). Solving semi-markov decision problems using average reward reinforcement learning. Management Science, 45(4), 560–574. https://doi.org/10.1287/mnsc.45.4.560.
Article
Google Scholar
de Laat, P. B. (2018). Algorithmic decision-making based on machine learning from big data: Can transparency restore accountability? Philosophy & Technology, 31(4), 525–541. https://doi.org/10.1007/s13347-017-0293-z.
Article
Google Scholar
Decker, S., & Saitto, S. (2014). Uber seeks to patent pricing surges that critics call gouging. Bloomberg. Retrieved May 3, 2018 from https://www.bloomberg.com/news/articles/2014-12-18/uber-seeks-to-patent-pricing-surges-that-critics-call-gouging.
den Boer, A. V. (2015). Dynamic pricing and learning: Historical origins, current research, and new directions. Surveys in Operations Research and Management Science, 20(1), 1–18. https://doi.org/10.1016/j.sorms.2015.03.001.
Article
Google Scholar
Dierksmeier, C. (2016). Reframing economic ethics: The philosophical foundations of humanistic management. London/New York: Palgrav Macmillan Publishers.
Book
Google Scholar
Dierksmeier, C. (2018). Qualitative freedom and cosmopolitan responsibility. Humanistic Management Journal, 2(2), 109–123. https://doi.org/10.1007/s41463-017-0029-3.
Article
Google Scholar
Dierksmeier, C., & Seele, P. (2016). Cryptocurrencies and business ethics. Journal of Business Ethics. https://doi.org/10.1007/s10551-016-3298-0.
Article
Google Scholar
Dilmé, F., & Li, F. (2019). Revenue management without commitment: Dynamic pricing and periodic flash sales. The Review of Economic Studies, 86(5), 1999–2034. https://doi.org/10.1093/restud/rdy073.
Article
Google Scholar
Dopfer, K., Foster, J., & Potts, J. (2004). Micro–meso–macro. Journal of Evolutionary Economics, 14(3), 263–279. https://doi.org/10.1007/s00191-004-0193-0.
Article
Google Scholar
Elegido, J. M. (2009). The just price: Three insights from the Salamanca School. Journal of Business Ethics, 90(1), 29–46. https://doi.org/10.1007/s10551-008-0024-6.
Article
Google Scholar
Elegido, J. M. (2011). The ethics of price discrimination. Business Ethics Quarterly, 21(04), 633–660. https://doi.org/10.5840/beq201121439.
Article
Google Scholar
Elegido, J. M. (2015). The just price as the price obtainable in an open market. Journal of Business Ethics, 130(3), 557–572. https://doi.org/10.1007/s10551-014-2240-6.
Article
Google Scholar
Ellickson, P. B., Misra, S., & Nair, H. S. (2012). Repositioning dynamics and pricing strategy. Journal of Marketing Research, 49(6), 750–772. https://doi.org/10.1509/jmr.11.0068.
Article
Google Scholar
Elmaghraby, W., & Keskinocak, P. (2003). Dynamic pricing in the presence of inventory considerations: Research overview, current practices, and future directions. Management Science, 49(10), 1287–1309. https://doi.org/10.1287/mnsc.49.10.1287.17315.
Article
Google Scholar
Esposito, E. (2017). Algorithmic memory and the right to be forgotten on the web. Big Data & Society, 4(1), 205395171770399. https://doi.org/10.1177/2053951717703996.
Article
Google Scholar
Esteves, R. B. (2014). Price discrimination with private and imperfect information. Scandinavian Journal of Economics, 116(3), 766–796. https://doi.org/10.1111/sjoe.12061.
Article
Google Scholar
Ettl, M., Harsha, P., Papush, A., & Perakis, G. (2019). A data-driven approach to personalized bundle pricing and recommendation. Manufacturing & Service Operations Management. https://doi.org/10.1287/msom.2018.0756.
Article
Google Scholar
Ezrachi, A., & Stucke, M. E. (2016). The rise of behavioural discrimination. European Competition Law Review, 37(12), 485–492. https://doi.org/10.2139/ssrn.2830206.
Article
Google Scholar
Faruqui, A., Hledik, R., & Tsoukalis, J. (2009). The power of dynamic pricing. The Electricity Journal, 22(3), 42–56. https://doi.org/10.1016/j.tej.2009.02.011.
Article
Google Scholar
Faruqui, A., & Palmer, J. (2011). Dynamic Pricing of Electricity and its Discontents. The Brattle Group, online document. Retrieved December 10, 2016 from http://assets.fiercemarkets.net/public/smartgridnews/Dynamic_Pricing_of_Electricity_and_its_Discontents_1.pdf.
Faruqui, A., & Sergici, S. (2010). Household response to dynamic pricing of electricity: A survey of 15 experiments. Journal of Regulatory Economics, 38(2), 193–225. https://doi.org/10.1007/s11149-010-9127-y.
Article
Google Scholar
Faruqui, A., & Sergici, S. (2013). Arcturus: International evidence on dynamic pricing. The Electricity Journal, 26(7), 55–65. https://doi.org/10.1016/j.tej.2013.07.007.
Article
Google Scholar
Feldman, D., Trzcinka, C., & Winer, R. S. (2015). Pricing under noisy signaling. Review of Quantitative Finance and Accounting, 45(2), 435–454. https://doi.org/10.1007/s11156-014-0442-8.
Article
Google Scholar
Feng, Q. (2010). Integrating dynamic pricing and replenishment decisions under supply capacity uncertainty. Management Science, 56(12), 2154–2172. https://doi.org/10.1287/mnsc.1100.1238.
Article
Google Scholar
Feng, L., Zhang, J., & Tang, W. (2015). A joint dynamic pricing and advertising model of perishable products. Journal of the Operational Research Society, 66(8), 1341–1351. https://doi.org/10.1057/jors.2014.89.
Article
Google Scholar
Fisher, M., Gallino, S., & Li, J. (2018). Competition-based dynamic pricing in online retailing: A methodology validated with field experiments. Management Science, 64(6), 2496–2514. https://doi.org/10.1287/mnsc.2017.2753.
Article
Google Scholar
Gal, M. S. (2017). Algorithmic-facilitated coordination: Market and legal solutions. CPI Antitrust Chronicle, May. Retrieved from https://www.competitionpolicyinternational.com/wp-content/uploads/2017/05/CPI-Gal.pdf.
Gal, M. S. (2019). Illegal pricing algorithms. Communications of the ACM, 62(1), 18–20. https://doi.org/10.1145/3292515.
Article
Google Scholar
Gallego, G., & Talebian, M. (2012). Demand learning and dynamic pricing for multi-version products. Journal of Revenue and Pricing Management, 11(3), 303–318. https://doi.org/10.1057/rpm.2010.36.
Article
Google Scholar
Gallego, G., & van Ryzin, G. (1994). Optimal dynamic pricing of inventories with stochastic demand over finite horizons. Management Science, 40(8), 999–1020. https://doi.org/10.1287/mnsc.40.8.999.
Article
Google Scholar
Garbarino, E., & Lee, O. F. (2003). Dynamic pricing in internet retail: Effects on consumer trust. Psychology and Marketing, 20(6), 495–513. https://doi.org/10.1002/mar.10084.
Article
Google Scholar
Garbarino, E., & Maxwell, S. (2010). Consumer response to norm-breaking pricing events in e-commerce. Journal of Business Research, 63(9–10), 1066–1072. https://doi.org/10.1016/j.jbusres.2008.12.010.
Article
Google Scholar
Gosavi, A. (2004a). Reinforcement learning for long-run average cost. European Journal of Operational Research, 155(3), 654–674. https://doi.org/10.1016/S0377-2217(02)00874-3.
Article
Google Scholar
Gosavi, A. (2004b). A reinforcement learning algorithm based on policy iteration for average reward: Empirical results with yield management and convergence analysis. Machine Learning, 55(1), 5–29. https://doi.org/10.1023/B:MACH.0000019802.64038.6c.
Article
Google Scholar
Gössling, T., & van Liedekerke, L. (2014). Editorial: The caring organisation. Journal of Business Ethics, 120(4), 437–440. https://doi.org/10.1007/s10551-014-2158-z.
Article
Google Scholar
Gratwohl, N. (2019). Der kassenlose Laden kommt in die Schweiz: Wird bald auch Gesichtserkennung eingesetzt? Neue Zürcher Zeitung. Retrieved from August 27, 2019 from https://www.nzz.ch/wirtschaft/kassenlose-laeden-sind-auf-dem-vormarsch-ld.1504205.
Green, P. E. (1963). Bayesian decision theory in pricing strategy. Journal of Marketing, 27(1), 5. https://doi.org/10.2307/1248574.
Article
Google Scholar
Gu, Y., & Wenzel, T. (2014). Strategic obfuscation and consumer protection policy. The Journal of Industrial Economics, 62(4), 632–660. https://doi.org/10.1111/joie.12060.
Article
Google Scholar
Haws, K. L., & Bearden, W. O. (2006). Dynamic pricing and consumer fairness perceptions. Journal of Consumer Research, 33(3), 304–311. https://doi.org/10.1086/508435.
Article
Google Scholar
Helberger, N. (2013). Freedom of expression and the Dutch Cookie-Wall. Amsterdam Law School, Paper (No. 2013-66). https://doi.org/10.2139/ssrn.2197251.
Helbing, D., & Seele, P. (2018). When code is law, algorithms must be made transparent. Retrieved April 12, 2019 from http://futurict.blogspot.com/2018/12/when-code-is-law-algorithms-must-be.html.
Hinz, O., Hann, I. H., & Spann, M. (2011). Price discrimination in E-commerce? an examination of dynamic pricing in name-your-own price markets. MIS Quarterly, 35(1), 81. https://doi.org/10.2307/23043490.
Article
Google Scholar
Ho, Y.-C., Ho, Y.-J., & Tan, Y. (2017). Online cash-back shopping: Implications for consumers and e-businesses. Information Systems Research, 28(2), 250–264. https://doi.org/10.1287/isre.2017.0693.
Article
Google Scholar
Hoffmann, F., Inderst, R., & Ottaviani, M. (2013). Hypertargeting, limited attention, and privacy: Implications for marketing and campaigning. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2395208.
Article
Google Scholar
Huang, K. (2010). Equilibrium market segmentation for targeted pricing based on customer characteristics. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1292508.
Article
Google Scholar
Huang, Y.-S., Hsu, C.-S., & Ho, J.-W. (2014). Dynamic pricing for fashion goods with partial backlogging. International Journal of Production Research, 52(14), 4299–4314. https://doi.org/10.1080/00207543.2014.881576.
Article
Google Scholar
Ittoo, A., & Petit, N. (2017). Algorithmic pricing agents and tacit collusion: A technological perspective. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3046405.
Article
Google Scholar
Johnson, D. G. (2015). Technology with no human responsibility? Journal of Business Ethics, 127(4), 707–715. https://doi.org/10.1007/s10551-014-2180-1.
Article
Google Scholar
Kalaycı, K. (2016). Confusopoly: Competition and obfuscation in markets. Experimental Economics, 19(2), 299–316. https://doi.org/10.1007/s10683-015-9438-z.
Article
Google Scholar
Karr, D. (2018). How to use algorithmic pricing to maximize profits. MarTech Blog. Retrieved July 2, 2018 from https://martech.zone/algorithmic-pricing-maximize-profits/.
Keskin, N. B., & Zeevi, A. (2014). Dynamic pricing with an unknown demand model: Asymptotically optimal semi-myopic policies. Operations Research, 62(5), 1142–1167. https://doi.org/10.1287/opre.2014.1294.
Article
Google Scholar
Kincaid, W. M., & Darling, D. A. (1963). An inventory pricing problem. Journal of Mathematical Analysis and Applications, 7(2), 183–208. https://doi.org/10.1016/0022-247X(63)90047-7.
Article
Google Scholar
Koh, B., Raghunathan, S., & Nault, B. R. (2017). Is voluntary profiling welfare enhancing? MIS Quarterly, 41(1), 23–41. https://doi.org/10.25300/MISQ/2017/41.1.02.
Article
Google Scholar
Kopalle, P. K., Rao, A. G., & Assunção, J. L. (1996). Asymmetric reference price effects and dynamic pricing policies. Marketing Science, 15(1), 60–85. https://doi.org/10.1287/mksc.15.1.60.
Article
Google Scholar
Kraemer, F., van Overveld, K., & Peterson, M. (2011). Is there an ethics of algorithms? Ethics and Information Technology, 13(3), 251–260. https://doi.org/10.1007/s10676-010-9233-7.
Article
Google Scholar
Kremer, M., Mantin, B., & Ovchinnikov, A. (2017). Dynamic pricing in the presence of myopic and strategic consumers: Theory and experiment. Production and Operations Management, 26(1), 116–133. https://doi.org/10.1111/poms.12607.
Article
Google Scholar
Kummer, M., & Schulte, P. (2019). When private information settles the bill: Money and privacy in Google’s market for smartphone applications. Management Science, 65(8), 3470–3494. https://doi.org/10.1287/mnsc.2018.3132.
Article
Google Scholar
Lee, D. J., Ahn, J. H., & Bang, Y. (2011). Managing consumer privacy concerns in personalization: A strategic analysis of privacy protection. MIS Quarterly, 35(2), 423. https://doi.org/10.2307/23044050.
Article
Google Scholar
Lee, F., & Monroe, K. B. (2008). Dynamic pricing on the internet: A price framing approach. Advances in Consumer Research, 35(2005), 637–638. http://search.ebscohost.com/login.aspx?direct=true&db=buh&AN=35063834&site=ehost-live.
Leonhardt, D. (2005). Why variable pricing fails at the vending machine. The New York Times. Retrieved June 10, 2019 from https://www.nytimes.com/2005/06/27/business/why-variable-pricing-fails-at-the-vending-machine.html.
Levin, Y., McGill, J., & Nediak, M. (2008). Risk in revenue management and dynamic pricing. Operations Research, 56(2), 326–343. https://doi.org/10.1287/opre.1070.0438.
Article
Google Scholar
Li, T., Sethi, S. P., & He, X. (2015a). Dynamic pricing, production, and channel coordination with stochastic learning. Production and Operations Management, 24(6), 857–882. https://doi.org/10.1111/poms.12320.
Article
Google Scholar
Li, S., Zhang, J., & Tang, W. (2015b). Joint dynamic pricing and inventory control policy for a stochastic inventory system with perishable products. International Journal of Production Research, 53(10), 2937–2950. https://doi.org/10.1080/00207543.2014.961206.
Article
Google Scholar
Liu, Q., & van Ryzin, G. J. (2008). Strategic capacity rationing to induce early purchases. Management Science, 54(6), 1115–1131. https://doi.org/10.1287/mnsc.1070.0832.
Article
Google Scholar
Maglaras, C., & Meissner, J. (2006). Dynamic pricing strategies for multiproduct revenue management problems. Manufacturing & Service Operations Management, 8(2), 136–148. https://doi.org/10.1287/msom.1060.0105.
Article
Google Scholar
Martin, K. (2018). Ethical implications and accountability of algorithms. Journal of Business Ethics. https://doi.org/10.1007/s10551-018-3921-3.
Article
Google Scholar
Martin, K. (2019a). Designing ethical algorithms. MIS Quarterly Executive, 2019(2016), 129–142. https://doi.org/10.17705/2msqe.00012.
Article
Google Scholar
Martin, N. (2019). Uber charges more if they think you’re willing to pay more. Forbes. Retrieved July 28, 2019 from https://www.forbes.com/sites/nicolemartin1/2019/03/30/uber-charges-more-if-they-think-youre-willing-to-pay-more/#17206e573654.
Martin, K. D., & Murphy, P. E. (2017). The role of data privacy in marketing. Journal of the Academy of Marketing Science, 45(2), 135–155. https://doi.org/10.1007/s11747-016-0495-4.
Article
Google Scholar
Matsumura, T., & Matsushima, N. (2015). Should firms employ personalized pricing? Journal of Economics & Management Strategy, 24(4), 887–903. https://doi.org/10.1111/jems.12109.
Article
Google Scholar
Maxwell, S., & Garbarino, E. (2010). The identification of social norms of price discrimination on the internet. Journal of Product & Brand Management, 19(3), 218–224. https://doi.org/10.1108/10610421011046193.
Article
Google Scholar
Mehra, S. K. (2016). Antitrust and the robo-seller: Competition in the time of algorithms. CPI Antitrust Chronicle, 100(4), 1323–1375. http://www.minnesotalawreview.org/wp-content/uploads/2016/04/Mehra_ONLINEPDF1.pdf.
Mercier-Roy, M., & Mailhot, C. (2019). What’s in an app? Investigating the moral struggles behind a sharing economy device. Journal of Business Ethics. https://doi.org/10.1007/s10551-019-04207-7.
Article
Google Scholar
Miklós-Thal, J., & Tucker, C. (2019). Collusion by algorithm: Does better demand prediction facilitate coordination between sellers? Management Science, 65(4), 1552–1561. https://doi.org/10.1287/mnsc.2019.3287.
Article
Google Scholar
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 1–21. https://doi.org/10.1177/2053951716679679.
Article
Google Scholar
Monsalve, F. (2014). Scholastic just price versus current market price: Is it merely a matter of labelling? European Journal of the History of Economic Thought, 21(1), 4–20. https://doi.org/10.1080/09672567.2012.683019.
Article
Google Scholar
Obermiller, C., Arnesen, D., & Cohen, M. (2012). Customized pricing: Win-win or end run? Drake Management Review, 1(2), 12–28.
Google Scholar
Oxera Consulting LLP. (2017). When algorithms set prices: winners and losers. Discussion paper. Retrieved June 25, 2018 from https://www.oxera.com/Latest-Thinking/Agenda/2017/When-algorithms-set-prices-winners-and-losers.aspx.
Oxford Dictionary. (2019). Algorithm: Definition of algorithm. Retrieved September 5, 2019 from https://www.lexico.com/en/definition/algorithm.
Özer, Ö., & Zheng, Y. (2016). Markdown or everyday low price? The role of behavioral motives. Management Science, 62(2), 326–346. https://doi.org/10.1287/mnsc.2014.2147.
Article
Google Scholar
Peura, H., & Bunn, D. W. (2015). Dynamic pricing of peak production. Operations Research, 63(6), 1262–1279. https://doi.org/10.1287/opre.2015.1429.
Article
Google Scholar
Phillips, R. L. (2005). Pricing and revenue optimization. Stanford, CA: Stanford University Press.
Book
Google Scholar
PYMNTS. (2018). In-store tracking tech gets personalized. PYMNTS.com. Retrieved August 25, 2019 from https://www.pymnts.com/news/retail/2018/adobe-retail-apocalypse-personalization-brick-and-mortar/.
Radner, R., Radunskaya, A., & Sundararajan, A. (2014). Dynamic pricing of network goods with boundedly rational consumers. Proceedings of the National Academy of Sciences, 111(1), 99–104. https://doi.org/10.1073/pnas.1319543110.
Article
Google Scholar
Ray, S., Li, S., & Song, Y. (2005). Tailored supply chain decision making under price-sensitive stochastic demand and delivery uncertainty. Management Science, 51(12), 1873–1891. https://doi.org/10.1287/mnsc.1050.0452.
Article
Google Scholar
Rayna, T., Darlington, J., & Striukova, L. (2015). Pricing music using personal data: Mutually advantageous first-degree price discrimination. Electronic Markets, 25(2), 139–154. https://doi.org/10.1007/s12525-014-0165-7.
Article
Google Scholar
Richards, T. J., Liaukonyte, J., & Streletskaya, N. A. (2016). Personalized pricing and price fairness. International Journal of Industrial Organization, 44, 138–153. https://doi.org/10.1016/j.ijindorg.2015.11.004.
Article
Google Scholar
Robbins, H., & Monro, S. (1951). A stochastic approximation method. The Annals of Mathematical Statistics, 22(3), 400–407. https://www.jstor.org/stable/2236626.
Rochelle, M. (2019). Press & media. Wasteless. Retrieved July 29, 2019 from https://www.wasteless.co/press.
Rossi, P. E., Allenby, G. M., & McCulloch, R. E. (2012). Bayesian statistics and marketing. Chichester: Wiley.
Google Scholar
Rust, R. T., & Chung, T. S. (2006). Marketing models of service and relationships. Marketing Science, 25(6), 560–580. https://doi.org/10.1287/mksc.1050.0139.
Article
Google Scholar
Schechner, S. (2017). Why do gas station prices constantly change? Blame the algorithm. The Wall Street Journal. Retrieved August 18, 2019 from https://www.wsj.com/articles/why-do-gas-station-prices-constantly-change-blame-the-algorithm-1494262674.
Schmidt, F. L., & Hunter, J. E. (2014). Methods of meta-analysis. London: SAGE Publications Inc.
Google Scholar
Schwalbe, U. (2018). Algorithms, machine learning, and collusion. Journal of Competition Law & Economics, 14(4), 568–607. https://doi.org/10.1093/joclec/nhz004.
Article
Google Scholar
Schwartz, A. (1993). A reinforcement learning method for maximizing undiscounted rewards. In Machine Learning Proceedings 1993 (pp. 298–305). Amsterdam: Elsevier. https://doi.org/10.1016/b978-1-55860-307-3.50045-9.
Snyder, J. (2012). What’s the matter with price gouging? Business Ethics Quarterly, 19(02), 275–293. https://doi.org/10.5840/beq200919214.
Article
Google Scholar
Song, Y., Ray, S., & Boyaci, T. (2009). Technical note: Optimal dynamic joint inventory-pricing control for multiplicative demand with fixed order costs and lost sales. Operations Research, 57(1), 245–250. https://doi.org/10.1287/opre.1080.0530.
Article
Google Scholar
Soo, Z. (2017). BingoBox to expand its unstaffed store concept beyond mainland China. South China Morning Post. Retrieved January 29, 2019 from http://www.scmp.com/tech/start-ups/article/2121799/bingobox-bring-unmanned-convenience-stores-hong-kong-next-year.
Spann, M., Fischer, M., & Tellis, G. J. (2015). Skimming or penetration? Strategic dynamic pricing for new products. Marketing Science, 34(2), 235–249. https://doi.org/10.1287/mksc.2014.0891.
Article
Google Scholar
Steppe, R. (2017). Online price discrimination and personal data: A general data protection regulation perspective. Computer Law and Security Review, 33(6), 768–785. https://doi.org/10.1016/j.clsr.2017.05.008.
Article
Google Scholar
Stucke, M. E., & Ezrachi, A. (2016). How pricing bots could form cartels and make things more expensive. Harvard Business Review. Retrieved July 15, 2019 from https://hbr.org/2016/10/how-pricing-bots-could-form-cartels-and-make-things-more-expensive.
Suddaby, R., Bitektine, A., & Haack, P. (2017). Legitimacy. Academy of Management Annals, 11(1), 451–478.
Article
Google Scholar
Tan, Y. R., Paul, A. A., Deng, Q., & Wei, L. (2017). Mitigating inventory overstocking: Optimal order-up-to level to achieve a target fill rate over a finite horizon. Production and Operations Management, 26(11), 1971–1988. https://doi.org/10.1111/poms.12750.
Article
Google Scholar
Tanner, A. (2014). Different customers, different prices, thanks to big data. Forbes. Retrieved August 18, 2019 from https://www.forbes.com/sites/adamtanner/2014/03/26/different-customers-different-prices-thanks-to-big-data/#6c6d11505730.
Thaler, R. (1985). Mental accounting and consumer choice. Marketing Science, 4(3), 199–214. https://doi.org/10.1287/mksc.4.3.199.
Article
Google Scholar
Townley, C., Morrison, E., & Yeung, K. (2017). Big data and personalized price discrimination in EU Competition law. Yearbook of European Law, 36(1), 683–748. https://doi.org/10.1093/yel/yex015.
Article
Google Scholar
UK Competition and Markets Authority. (2018). Pricing algorithms: Economic working paper on the use of algorithms to facilitate collusion and personalised pricing. Crown. Retrieved July 25, 2019 from https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/746353/Algorithms_econ_report.pdf.
van Ryzin, G., & McGill, J. (2000). Revenue management without forecasting or optimization: An adaptive algorithm for determining airline seat protection levels. Management Science, 46(6), 760–775. https://doi.org/10.1287/mnsc.46.6.760.11936.
Article
Google Scholar
Van Uytsel, S. (2018). Artificial intelligence and collusion: A literature overview. In Robotics, AI and the future of law, perspectives in law, business and innovation (pp. 155–182). Singapore: Springer. https://doi.org/10.1007/978-981-13-2874-9_7.
Waldfogel, J. (2015). First degree price discrimination goes to school. Journal of Industrial Economics, 63(4), 569–597. https://doi.org/10.1111/joie.12085.
Article
Google Scholar
Weisstein, F. L., Monroe, K. B., & Kukar-Kinney, M. (2013). Effects of price framing on consumers’ perceptions of online dynamic pricing practices. Journal of the Academy of Marketing Science, 41(5), 501–514. https://doi.org/10.1007/s11747-013-0330-0.
Article
Google Scholar
Wolak, F. (2016). Designing nonlinear price schedules for urban water utilities to balance revenue and conservation goals. National Bureau of Economic Research Working Paper 22503, 1–41. https://doi.org/10.1017/cbo9781107415324.004.
Wu, S., Hitt, L. M., Chen, P., & Anandalingam, G. (2008). Customized bundle pricing for information goods: A nonlinear mixed-integer programming approach. Management Science, 54(3), 608–622. https://doi.org/10.1287/mnsc.1070.0812.
Article
Google Scholar
Xia, F., Chatterjee, R., & May, J. H. (2019). Using conditional restricted boltzmann machines to model complex consumer shopping patterns. Marketing Science, 38(4), 711–727. https://doi.org/10.1287/mksc.2019.1162.
Article
Google Scholar
Xia, L., Monroe, K. B., & Cox, J. L. (2004). The price is unfair! A conceptual framework of price fairness perceptions. Journal of Marketing, 68(4), 1–15. https://doi.org/10.1509/jmkg.68.4.1.42733.
Article
Google Scholar
Xiao, G., Yang, N., & Zhang, R. (2015). Dynamic pricing and inventory management under fluctuating procurement costs. Manufacturing & Service Operations Management, 17(3), 321–334. https://doi.org/10.1287/msom.2015.0519.
Article
Google Scholar
Xiong, Y., Li, G., & Fernandes, K. J. (2010). Dynamic pricing model and algorithm for perishable products with fuzzy demand. Applied Stochastic Models in Business and Industry, 26(6), 758–774. https://doi.org/10.1002/asmb.816.
Article
Google Scholar
Xu, Z., & Dukes, A. (2019). Product line design under preference uncertainty using aggregate consumer data. Marketing Science, 38(4), 669–689. https://doi.org/10.1287/mksc.2019.1160.
Article
Google Scholar
Zenger, H. (2012). The marginal price effects of antitrust rules against price discrimination. Economics Letters, 117(3), 921–923. https://doi.org/10.1016/j.econlet.2012.07.017.
Article
Google Scholar
Zerilli, J., Knott, A., Maclaurin, J., & Gavaghan, C. (2018). Transparency in algorithmic and human decision-making: Is there a double standard? Philosophy & Technology. https://doi.org/10.1007/s13347-018-0330-6.
Article
Google Scholar
Zhang, J. (2011). The perils of behavior-based personalization. Marketing Science, 30(1), 170–186. https://doi.org/10.1287/mksc.1100.0607.
Article
Google Scholar
Zhang, Y., Mantin, B., & Wu, Y. (2019). Inventory decisions in the presence of strategic customers: Theory and behavioral evidence. Production and Operations Management, 28(2), 374–392. https://doi.org/10.1111/poms.12926.
Article
Google Scholar
Zhou, S. X., & Chao, X. (2014). Dynamic pricing and inventory management with regular and expedited supplies. Production and Operations Management, 23(1), 65–80. https://doi.org/10.1111/poms.12047.
Article
Google Scholar
Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. London: Profile Books Ltd. https://www.publicaffairsbooks.com/titles/shoshana-zuboff/the-age-of-surveillance-capitalism/9781610395694/.
Zuiderveen Borgesius, F., & Poort, J. (2017). Online price discrimination and EU data privacy law. Journal of Consumer Policy, 40(3), 347–366. https://doi.org/10.1007/s10603-017-9354-z.
Article
Google Scholar
Zwolinski, M. (2008). The ethics of price gouging. Business Ethics Quarterly, 18(03), 347–378. https://doi.org/10.5840/beq200818327.
Article
Google Scholar
Zwolinski, M. (2009). Dialogue on price gouging. Business Ethics Quarterly, 19(2), 295–303.
Article
Google Scholar