International Journal of Data Science and Analysis
Volume 6, Issue 1, February 2020, Pages: 12-19
Received: Dec. 29, 2019;
Accepted: Jan. 8, 2020;
Published: Feb. 4, 2020
Views 560 Downloads 219
Inga Toma, Business Administration, International University of Monaco, Principality of Monaco, Monaco
Dursun Delen, Management Science and Information Systems, Spears School of Business, Oklahoma State University, Tulsa, OK, US
Gregory Moscato, Business Administration, International University of Monaco, Principality of Monaco, Monaco
Recent forecasting research has shown a paradigm shift from algorithm aversion to appreciation. Despite growing trust in technological decision support, business decisions are often made based on gut feeling and intuition, ignoring part or all of the available data and information. Creating effective decision support solutions necessitates the understanding of the impact of emerging artificial intelligence and machine learning technologies on business decision-making processes. This study examines whether forecasting information delivery at a time when a business decision is made influences or changes the decision maker’s mind, thereby leading to a different decision. The study employs a 2 × 2 between-subject experimental setting where forecasted results (gain/loss) and automated advice (risk/certainty) were crossed-examined. A sample of 137 participants was asked to make four different product price change decisions assisted by automated decision aid. The experiment involved two independent samples, one taken from Amazon Mechanical Turk workers and the other from the members of LinkedIn managerial groups. Results show that decision-makers are more likely to rely on automated recommendation and change their initial decision when forecasted decision outcomes lead to gain, whereas they would discount algorithmic aid if a loss is forecasted. This research adds to the extant literature in the field of human-technology interactions and contributes to the descriptive and prescriptive decision theories by illustrating that gain forecasting has a higher impact on the algorithm appreciation than loss forecasting.
Impact of Loss and Gain Forecasting on the Behavior of Pricing Decision-making, International Journal of Data Science and Analysis.
Vol. 6, No. 1,
2020, pp. 12-19.
B. J. Dietvorst, J. P. Simmons, and C. Massey, "Algorithm Aversion: People Erroneously Avoid Algorithms After Seeing Them Err" in Journal of Experimental Psychology: General, vol. 144 no. 1, 2015, pp. 114–126.
J. M. Logg, J. A. Minson, and D. A. Moore, "Algorithm Appreciation: People Prefer Algorithmic To Human Judgment" in Harvard Business School NOM Unit Working Paper no. 17-086. 2018. http://dx.doi.org/10.2139/ssrn.2941774.
F. Artinger, M. Petersen, G. Gigerenzer, and J. Weibler, "Heuristics as adaptive decision strategies in management", in Journal of Organizational Behavior, vol. 36, 2015, pp. 33-52. http://doi.org/10.1002/job.1950.
D. Kahneman and A. Tversky, "Prospect Theory: An Analysis of Decision under Risk" in Econometrica, vol. 47 no. 2, 1979, pp. 263-291.
G. Gigerenzer, Risk Savy - How to Make Good Decisions, London: Penguin Books Ltd, 2015.
A. Gajewar, J. Yang, D. Chen, and P. Chen, "Patent Application Publication Pricing Engine Revenue Evaluation", United States, 2013, available at: https://patentimages.storage.googleapis.com/17/61/e8/012aff2c6dd394/US20130290069A1.pdf (accessed 16 September 2019).
D. Waddell and A. S. Sohal, "Forecasting: The Key to Managerial Decision Making" in Management Decision, vol. 32 no. 1, 1994, pp. 41-49.
P. De, Y. Hu, and M. S. Rahman, "Technology Usage and Online Sales: An Empirical Study" in Management Science, vol. 56 no. 11, 2010, pp. 1930–1945.
Dzindolet, M. T., Pierce, L. G., Beck, H. P. and Dawe, L. A. (2002), "The Perceived Utility of Human and Automated Aids in a Visual Detection Task", Human Factors, Vol. 44 No. 1, pp. 79–94. http://dx.doi.org/10.1518/0018720024494856.
Fildes, R. and Goodwin, P. (2007), "Against Your Better Judgment? How Organizations Can Improve Their Use of Management Judgment in Forecasting", INFORMS Journal on Applied Analytics, Vol. 37 No. 6, pp. 570–576. https://doi.org/10.1287/inte.1070.0309.
Yeomans, M., Shah, A., Mullainathan, S. and Kleinberg, J. (2019), "Making Sense of Recommendations", Journal of Behavioral Decision Making, Vol. 32 No. 4, pp. 403-414 https://doi.org/10.1002/bdm.2118.
Dietvorst, B. J., Simmons, J. P. and Massey, C. (2016), "Overcoming Algorithm Aversion: People will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them", Management Science, Vol. 64 No. 3, pp. 1155-1170. http://dx.doi.org/10.1287/mnsc.2016.2643.
Prahl, A. and Van Swol, L. (2017), "Understanding algorithm aversion: When is advice from automation discounted?", Journal of Forecasting, Vol. 36 No. 6, pp. 691–702. http://doi.org/10.1002/for.2464.
Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J. and Mullainathan, S. (2018), "Human Decisions and Machine Predictions", The Quarterly Journal of Economics, Vol. 133 No. 1, pp. 237–293. http://doi.org/10.1093/qje/qjx032.
Keynes, J. M. (1921), A treatise on probability, Macmillan and Co., London, England.
Knight, F. H. (1921), Risk, Uncertainty and Profit, Houghton Mifflin, Boston.
Faulkner, P., Feduzi, A. and Runde, J. (2017), "Unknowns, Black Swans and the risk/uncertainty distinction", Cambridge Journal of Economics, Vol. 41 No. 5, pp. 1279–1302. https://doi.org/10.1093/cje/bex035.
Luce, R. D. and Raiffa, H. (1957), Games and decisions: Introduction and critical survey, Wiley, Oxford, England.
Gomory, R. (1995), "The Known, the Unknown and the Unknowable", Scientific American, Vol. 272 No. 6, pp. 120. http://www.jstor.org/stable/24980843.
Diebold, F. X., Doherty, N. A. and Herring, R. J. (2010), The Known, the Unknown, and the Unknowable in Financial Risk Management, Princeton University Press.
Mousavi, S. and Gigerenzer, G. (2014), "Risk, uncertainty, and heuristics", Journal of Business Research, Vol. 67 No. 8, pp. 1671–1678. http://doi.org/10.1016/j.jbusres.2014.02.013.
Graham, J. R., Harvey, C. R. and Puri, M. (2015), "Capital allocation and delegation of decision-making authority within firms", Journal of Financial Economics, Vol. 115 No. 3, pp. 449–470 http://doi.org/10.1016/j.jfineco.2014.10.011.
Huang, L. and Pearce, J. L. (2015), "Managing the Unknowable: The Effectiveness of Early-stage Investor Gut Feel in Entrepreneurial Investment Decisions", Administrative Science Quarterly, Vol. 60 No. 4, pp. 634–670. http://doi.org/10.1177/0001839215597270.
Bonaccio, S. and Dalal, R. S. (2006), "Advice taking and decision-making: An integrative literature review, and implications for the organizational sciences", Organizational Behavior and Human Decision Processes, Vol. 101 No. 2, pp. 127–151. http://doi.org/10.1016/j.obhdp.2006.07.001.
Sanders, N. R. and Manrodt, K. B. (2003), "The efficacy of using judgmental versus quantitative forecasting methods in practice", Omega, Vol. 31 No. 6, pp. 511–522. http://doi.org/10.1016/j.omega.2003.08.007.
Tversky, A. and Kahneman, D. (1992), "Advances in Prospect Theory: Cumulative Representation of Uncertainty", Journal of Risk and Uncertainty, Vol. 5 No. 4, pp. 297-323. https://doi.org/10.1007/BF00122574.
Lipkus, I. M., Samsa, G. and Rimer, B. K. (2001), "General Performance on a Numeracy Scale among Highly Educated Samples", Medical Decision Making, Vol. 21 No. 1, pp. 37–44. https://doi.org/10.1177/0272989X0102100105.