When Will Workers Follow an Algorithm? A Field Experiment with a Retail Business

References

• Abeille M, Lazaric A (2017) Linear Thompson sampling revisited. Electronic J. Statist. 11(2):5165–5197.Crossref, Google Scholar
• Acemoglu D, Restrepo P (2018) The race between machine and man: Implications of technology for growth, factor shares and employment. Amer. Econom. Rev. 108(6):1488–1542. Google Scholar
• Aghion P, Espinosa MP, Jullien B (1993) Dynamic duopoly with learning through market experimentation. Econom. Theory 3(3):517–539.Crossref, Google Scholar
• Agrawal S, Goyal N (2012) Analysis of Thompson sampling for the multi-armed bandit problem. JMLR Workshop Conf. Proc. 23:39.1–39.26.Google Scholar
• Anand BN, Shachar R (2011) Advertising, the matchmaker. RAND J. Econom. 42(2):205–245.Crossref, Google Scholar
• Atkin D, Chaudhry A, Chaudry S, Khandelwal AK, Verhoogen E (2017) Organizational barriers to technology adoption: Evidence from soccer-ball producers in Pakistan. Quart. J. Econom. 132(3):1101–1164.Crossref, Google Scholar
• Bartling B, Fehr E, Herz H (2014) The intrinsic value of decision rights. Econometrica 82(6):2005–2039.Crossref, Google Scholar
• Bergemann D, Valimaki J (2000) Experimentation in markets. Rev. Econom. Stud. 67(2):213–234.Crossref, Google Scholar
• Bertocchi G, Spagat M (1998) Growth under uncertainty with experimentation. J. Econom. Dynam. Control 23(2):209–231.Crossref, Google Scholar
• Bolton P, Harris C (1999) Strategic experimentation. Econometrica 67(2):349–374.Crossref, Google Scholar
• Bubeck S, Cesa-Bianchi N (2012) Regret analysis of stochastic and nonstochastic multi-armed bandit problems. Foundations Trends Machine Learn. 5(1):1–122.Google Scholar
• Caro F, Gallien J (2007) Dynamic assortment with demand learning for seasonal consumer goods. Management Sci. 53(2):276–292.Link, Google Scholar
• Chapelle O, Li L (2011) An empirical evaluation of Thompson sampling. Shawe-Taylor J, Zemel RS, Bartlett PL, Pereira F, Weinberger KQ, eds. Advances in Neural Information Processing Systems, vol. 24 (Curran Associates, Red Hook, NY), 2249–2257.Google Scholar
• Chernew M, Gowrisankaran G, Scanlon DP (2008) Learning and the value of information: Evidence from health plan report cards. J. Econometrics 144(1):156–174.Crossref, Google Scholar
• Ching A, Ishihara M (2010) The effects of detailing on prescribing decisions under quality uncertainty. Quant. Marketing Econom. 8(2):123–165.Crossref, Google Scholar
• Ching AT (2010) Consumer learning and heterogeneity: Dynamics of demand for prescription drugs after patent expiration. Internat. J. Indust. Organ. 28(6):619–638.Crossref, Google Scholar
• Ching AT, Erdem T, Keane MP (2013) Learning models: An assessment of progress, challenges, and new developments. Marketing Sci. 32(6):913–938.Link, Google Scholar
• Coscelli A, Shum M (2004) An empirical model of learning and patient spillovers in new drug entry. J. Econometrics 122(2):213–246.Crossref, Google Scholar
• Crawford GS, Shum M (2005) Uncertainty and learning in pharmaceutical demand. Econometrica 73(4):1137–1173.Crossref, Google Scholar
• Creane A (1994) Experimentation with heteroskedastic noise. Econom. Theory 4(2):275–286.Crossref, Google Scholar
• Deneckere R, Marvel HP, Peck J (1996) Demand uncertainty, inventories, and resale price maintenance. Quart. J. Econom. 111(3):885–913.Crossref, Google Scholar
• Deneckere R, Marvel HP, Peck J (1997) Demand uncertainty and price maintenance: Markdowns as destructive competition. Amer. Econom. Rev. 87(4):619–641.Google Scholar
• Dietvorst BJ, Simmons JP, Massey C (2015) Algorithm aversion: People erroneously avoid algorithms after seeing them err. J. Experiment. Psych.: General 144(1):114–126.Crossref, Google Scholar
• Dietvorst BJ, Simmons JP, Massey C (2018) Overcoming algorithm aversion: People will use imperfect algorithms if they can (even slightly) modify them. Management Sci. 64(3):1155–1170.Link, Google Scholar
• Dzindolet MT, Pierce LG, Beck HP, Dawe LA (2002) The perceived utility of human and automated aids in a visual detection task. Human Factors 44(1):79–94.Crossref, Google Scholar
• Erdem T, Keane MP, Sun B (2008) A dynamic model of brand choice when price and advertising signal product quality. Marketing Sci. 27(6):1111–1125.Link, Google Scholar
• Erdem T, Keane MP, Öncü TS, Strebel J (2005) Learning about computers: An analysis of information search and technology choice. Quant. Marketing Econom. 3(3):207–247.Crossref, Google Scholar
• Fehr E, Herz H, Wilkening T (2013) The lure of authority: Motivation and incentive effects of power. Amer. Econom. Rev. 103(4):1325–1359.Crossref, Google Scholar
• Gallego G, Topaloglu H (2014) Constrained assortment optimization for the nested logit model. Management Sci. 60(10):2583–2601.Link, Google Scholar
• Gittins J, Jones D (1974) A dynamic allocation index for the sequential design of experiments. Gani J, ed. Progress in Statistics (North-Holland, Amsterdam), 241–266.Google Scholar
• Harpaz G, Lee WY, Winkler RL (1982) Learning, experimentation, and the optimal output decisions of a competitive firm. Management Sci. 28(6):589–603.Link, Google Scholar
• Harrington JE, Jr. (1995) Experimentation and learning in a differentiated-products duopoly. J. Econom. Theory 66(1):275–288.Crossref, Google Scholar
• Hitsch GJ (2006) An empirical model of optimal dynamic product launch and exit under demand uncertainty. Marketing Sci. 25(1):25–50.Link, Google Scholar
• Hoffman M, Kahn LB, Li D (2018) Discretion in hiring. Quart. J. Econom. 133(2):765–800.Crossref, Google Scholar
• Israel M (2005) Services as experience goods: An empirical examination of consumer learning in automobile insurance. Amer. Econom. Rev. 95(5):1444–1463.Crossref, Google Scholar
• Keller G, Rady S (2010) Strategic experimentation with Poisson bandits. Theoret. Econom. 5(2):275–311.Crossref, Google Scholar
• Keller G, Rady S, Martin M (2005) Strategic experimentation with exponential bandits. Econometrica 73(1):39–68.Crossref, Google Scholar
• May BC, Korda N, Lee A, Leslie DS (2012) Optimistic Bayesian sampling in contextual-bandit problems. J. Machine Learn. Res. 2012:2069–2106.Google Scholar
• Mirman LJ, Samuelson L, Urbano A (1993) Monopoly experimentation. Internat. Econom. Rev. 34(3):549–563.Crossref, Google Scholar
• Morita T, Takano Y, Nakajima T, Go H, Yasuoka H (2012) New marketing strategy using the electronic money at beverage vending machines. Working paper, Keio University, Tokyo, Japan.Google Scholar
• Narayanan S, Manchanda P (2009) Heterogeneous learning and the targeting of marketing communication for new products. Marketing Sci. 28(3):424–441.Link, Google Scholar
• Narayanan S, Manchanda P, Chintagunta PK (2005) Temporal differences in the role of marketing communication in new product categories. J. Marketing Res. 42(3):278–290.Crossref, Google Scholar
• Önkal D, Goodwin P, Thomson M, Gönül S, Pollock A (2009) The relative influence of advice from human experts and statistical methods on forecast adjustments. J. Behav. Decision Making 22(4):390–409.Crossref, Google Scholar
• Owens D, Grossman Z, Fackler R (2014) The control premium: A preference for payoff autonomy. Amer. Econom. J. 6(4):138–161.Google Scholar
• Prahl A, Van Swol L (2017) Understanding algorithm aversion: When is advice from automation discounted? J. Forecasting 36(6):691–702.Crossref, Google Scholar
• Raman K, Chatterjee R (1995) Optimal monopolist pricing under demand uncertainty in dynamic markets. Management Sci. 41(1):144–162.Link, Google Scholar
• Rob R (1991) Learning and capacity expansion under demand uncertainty. Rev. Econom. Stud. 58(4):655–675.Crossref, Google Scholar
• Rusmevichientong P, Shen Z-JM, Shmoys DB (2010) Dynamic assortment optimization with a multinomial logit choice model and capacity constraint. Oper. Res. 58(6):1666–1680.Link, Google Scholar
• Rusmevichientong P, Shmoys D, Tong C, Topaloglu H (2014) Assortment optimization under the multinomial logit model with random choice parameters. Prod. Oper. Management 23(11):2023–2039.Crossref, Google Scholar
• Scott SL (2010) A modern Bayesian look at the multi-armed bandit. Appl. Stochastic Models Bus. Indust. 26(6):639–658.Crossref, Google Scholar
• Szymanowski M, Gijsbrechts E (2012) Consumption-based cross-brand learning: Are private labels really private? J. Marketing Res. 49(2):231–246.Crossref, Google Scholar
• Thompson W (1933) On the likelihood that one unknown probability exceeds in view of the evidence of two samples. Biometrika 25(3/4):285–294.Crossref, Google Scholar
• Wang R (2012) Capacitated assortment and price optimization under the multinomial logit model. Oper. Res. Lett. 40(6):492–497.Google Scholar