Abstract
The plethora of comparison shopping agents (CSAs) in today’s markets enables buyers to query more than a single CSA when shopping, thus expanding the list of sellers whose prices they obtain. This potentially decreases the chance of a purchase within any single interaction between a buyer and a CSA, and consequently decreases each CSAs’ expected revenue per-query. Obviously, a CSA can improve its competence in such settings by acquiring more sellers’ prices, potentially resulting in a more attractive “best price”. In this paper we suggest a complementary approach that improves the attractiveness of the best result returned based on intelligently controlling the order according to which they are presented to the user, in a way that utilizes several known cognitive-biases of human buyers. The advantage of this approach is in its ability to affect the buyer’s tendency to terminate her search for a better price, hence avoid querying further CSAs, without spending valuable resources on finding additional prices to present. The effectiveness of our method is demonstrated using real data, collected from four CSAs for five products. Our experiments confirm that the suggested method effectively influence people in a way that is highly advantageous to the CSA compared to the common method for presenting the prices. Furthermore, we experimentally show that all of the components of our method are essential to its success.
Similar content being viewed by others
Notes
As for today, some of the common CSAs are PriceGrabber.com, Bizrate.com and Shopper.com.
In case the number of prices known to the CSA is odd, the lower part includes one additional price compared to the upper one.
In case the number of prices in this subset is odd, the anchor phase includes one additional price compared to the effort phase.
The raw data used for the experiments is available upon request from the corresponding author.
The videos are available upon request from the corresponding author.
References
Alkoby, S., Sarne, D., & Das, S. (2015). Strategic free information disclosure for search-based information platforms. In International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS) (pp. 635–643).
Amazon Mechanical Turk (AMT). https://www.mturk.com/mturk/.
Ariely, D., & Zakay, D. (2001). A timely account of the role of duration in decision making. Acta Psychologica, 108(2), 187–207.
Azaria, A., Aumann, Y., & Kraus, S. (2014). Automated agents for reward determination for human work in crowdsourcing applications. Autonomous Agents and Multi-Agent Systems, 28(6), 934–955.
Azaria, A., Gal, Y., Kraus, S., & Goldman, C.V. (2015). Strategic advice provision in repeated human-agent interactions. In Autonomous Agents and Multi-Agent Systems (pp. 1–26).
Azaria, A., Hassidim, A., Kraus, S., Eshkol, A., Weintraub, O., & Netanely, I. (2013). Movie recommender system for profit maximization. In Proceedings of RecSys, ACM (pp. 121–128).
Azaria, A., Rabinovich, Z., Goldman, C. V., & Kraus, S. (2014). Strategic information disclosure to people with multiple alternatives. ACM Transactions on Intelligent Systems and Technology, 5(4), 64:1–64:21.
Azaria, A., Richardson, A., & Kraus, S. (2014). An agent for the prospect presentation problem. In International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS) (pp. 989–996).
Bakos, J. (1997). Reducing buyer search costs: Implications for electronic marketplaces. Management Science, 43(12), 1676–1692.
Bar-Hillel, M. (2011). Location, location, location: Position effects in choice among simultaneously presented options. Tech. rep., The Center for the Study of Rationality.
Baumeister, R. (2003). The psychology of irrationality: Why people make foolish, self-defeating choices. The Psychology of Economics Decisions, 1, 3–16.
Bennett, P., Brennan, M., & Kearns, Z. (2003). Psychological aspects of price: An empirical test of order and range effects. Marketing Bulletin, 14, 1–8.
Bonnardel, N., Piolat, A., & Le Bigot, L. (2011). The impact of colour on website appeal and users cognitive processes. Displays, 32(2), 69–80.
Buhrmester, M., Kwang, T., & Gosling, S. D. (2011). Amazon’s mechanical turk a new source of inexpensive, yet high-quality, data? Perspectives on Psychological Science, 6(1), 3–5.
Clay, K., Krishnan, R., Wolff, E., & Fernandes, D. (2002). Retail strategies on the web: Price and non-price competition in the online book industry. Journal of Industrial Economics, 50, 351–367.
Decker, K., Sycara, K., & Williamson, M. (1997). Middle-agents for the internet. In Proceedings of the International Joint Conferences on Artificial Intelligence (IJCAI) (pp. 578–583).
Elmalech, A., Sarne, D., & Agmon, N. (2016). Agent development as a strategy shaper. In International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS) 30(3) (pp. 506–525).
Elmalech, A., Sarne, D., & Grosz, B. J. (2015). Problem restructuring for better decision making in recurring decision situations. Autonomous Agents and Multi-Agent Systems, 29(1), 1–39.
Elmalech, A., Sarne, D., Rosenfeld, A., & Erez, E.S. (2015). When suboptimal rules. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) (pp. 1313–1319). Citeseer.
Entin, E. E., & Serfaty, D. (1997). Sequential revision of belief: An application to complex decision making situations. Systems Man and Cybernetics, 27(3), 289–301.
Fogg, B.J. (2002). Persuasive technology: Using computers to change what we think and do. In Ubiquity, 2002 December, p. 5.
Garfinkel, R., Gopal, R., Pathak, B., & Yin, F. (2008). Shopbot 2.0: Integrating recommendations and promotions with comparison shopping. Decision Support Systems, 46(1), 61–69.
Grewal, D., Krishnan, R., Baker, J., & Borin, N. (1998). The effect of store name, brand name and price discounts on consumers’ evaluations and purchase intentions. Journal of Retailing, 74(3), 331–352.
Hajaj, C., Hazon, N., & Sarne, D. (2014). Ordering effects and belief adjustment in the use of comparison shopping agents. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) (pp. 930–936).
Hajaj, C., Hazon, N., & Sarne, D. (2015). Improving comparison shopping agents? competence through selective price disclosure. Electronic Commerce Research and Applications, 14(6), 563–581.
Hajaj, C., & Sarne, D. (2014). Strategic information platforms: Selective disclosure and the price of free. In Proceedings of the ACM Conference on Economics and Computation (EC), ACM (pp. 839–856).
He, M., Jennings, N. R., & Leung, H. (2003). On agent-mediated electronic commerce. IEEE Transaction on Knowledge and Data Engineering, 15(4), 985–1003.
Hogarth, R. M., & Einhorn, H. J. (1992). Order effects in belief updating: The belief-adjustment model. Cognitive Psychology, 24(1), 1–55.
Icard, T., Pacuit, E., & Shoham, Y. (2010). Joint revision of belief and intention. In Proceedings of Knowledge Representation and Reasoning (pp. 572–574).
Johnson, E. J., Moe, W. W., Fader, P. S., Bellman, S., & Lohse, G. L. (2004). On the depth and dynamics of online search behavior. Management Science, 50(3), 299–308.
Kahneman, D. (1992). Reference points, anchors, norms, and mixed feelings. Organizational Behavior and Human Decision Processes, 51(2), 296–312.
Karat, C. M., Blom, J. O., & Karat, J. (2004). Designing personalized user experiences in eCommerce., Human-Computer Interaction Series Dordrecht: Kluwer Academic.
Kephart, J. O., & Greenwald, A. R. (2002). Shopbot economics. Journal of Autonomous Agents and Multi-Agent Systems, 5(3), 255–287.
Knight, E. (2010). The Use of Price Comparison Sites in the UK General Insurance Market.
Krulwich, B. (1996). The bargainfinder agent: Comparison price shopping on the internet. In J. Williams (Ed.), Bots and Other Internet Beasties (pp. 257–263). Indianapolis: Sams Publishing. chap. 13.
Lau, R.Y. (2003). Belief revision for adaptive recommender agents in e-commerce. In Intelligent Data Engineering and Automated Learning, Springer (pp. 99–103).
Levy, P., & Sarne, D. (2016). Intelligent advice provisioning for repeated interaction. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI).
Li, Y. M., Wu, C. T., & Lai, C. Y. (2013). A social recommender mechanism for e-commerce: Combining similarity, trust, and relationship. Decision Support Systems, 55(3), 740–752.
Lieto, A., & Vernero, F. (2013). Unveiling the link between logical fallacies and web persuasion. In Proceedings of the 5th Annual ACM Web Science Conference, ACM (pp. 473–478).
Mandel, N., & Johnson, E. (1999). Constructing preferences online: can web pages change what you want? Working paper. University of Pennsylvania.
Markopoulos, P., & Kephart, J. (2002). How valuable are shopbots? In International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS) (pp. 1009–1016).
Markopoulos, P., & Ungar, L. (2001). Pricing price information in e-commerce. In Proceedings of the ACM Conference on Economics and Computation (EC) (pp. 260–263).
Markopoulos, P., & Ungar, L. (2002). Shopbots and pricebots in electronic service markets. In Game Theory and Decision Theory in Agent-Based Systems (pp. 177–195).
Mason, W., & Suri, S. (2012). Conducting behavioral research on amazon’s mechanical turk. Behavior Research Methods, 44(1), 1–23.
Menon, S., & Kahn, B. (2002). Cross-category effects of induced arousal and pleasure on the internet shopping experience. Journal of Retailing, 78(1), 31–40.
Monroe, K. B. (1990). Pricing: Making profitable decisions. New York: McGraw-Hill.
Moraga-Gonzalez, J. L., & Wildenbeest, M. (2012). Comparison sites. In M. Peitz & J. Waldfogel (Eds.), The oxford handbook of the digital economy. Oxford: Oxford University Press.
Nisbett, R. E., & Wilson, T. D. (1977). Telling more than we can know: Verbal reports on mental processes. Psychological Review, 84(3), 231.
Oestreicher-Singer, G., & Sundararajan, A. (2012). The visible hand? demand effects of recommendation networks in electronic markets. Management Science, 58(11), 1963–1981.
Paolacci, G., Chandler, J., & Ipeirotis, P. (2010). Running experiments on amazon mechanical turk. Judgment and Decision Making, 5(5), 411–419.
Pathak, B. (2010). A survey of the comparison shopping agent-based decisions support systems. Journal of Electronic Commerce Research, 11(3), 177–192.
Peled, N., Gal, Y. K., & Kraus, S. (2015). A study of computational and human strategies in revelation games. Autonomous Agents and Multi-Agent Systems, 29(1), 73–97.
Piercy, N. F., Cravens, D. W., & Lane, N. (2010). Thinking strategically about pricing decisions. Journal of Business Strategy, 31(5), 38–48.
Rao, A., & Monroe, K. (1989). The effect of price, brand name, and store name on buyers’ perceptions of product quality: An integrative review. Journal of Marketing Research, 26(3), 351–357.
Rochlin, I., & Sarne, D. (2015). Constraining information sharing to improve cooperative information gathering. Journal Artificial Intelligence Research, 54, 437–469.
Rochlin, I., Sarne, D., & Mash, M. (2014). Joint search with self-interested agents and the failure of cooperation enhancers. Artificial Intelligence, 214, 45–65.
Rosenfeld, A., Zuckerman, I., Segal-Halevi, E., Drein, O., & Kraus, S. (2016). Negochat-a: A chat-based negotiation agent with bounded rationality. Autonomous Agents and Multi-Agent Systems, 30(1), 60–81.
Dupin de Saint-Cyr, F., & Lang, J. (2011). Belief extrapolation (or how to reason about observations and unpredicted change). Artificial Intelligence, 175(2), 760–790.
Sarne, D. (2013). Competitive shopbots-mediated markets. ACM Transactions on Economics and Computation, 1(3), 17:1–17:41.
Sarne, D., Kraus, S., & Ito, T. (2007). Scaling-up shopbots: a dynamic allocation-based approach. In International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS) (pp. 338–345).
Shapiro, S., Pagnucco, M., Lespérance, Y., & Levesque, H. J. (2011). Iterated belief change in the situation calculus. Artificial Intelligence, 175(1), 165–192.
Tan, C. H., Goh, K. Y., & Teo, H. H. (2010). Effects of comparison shopping websites on market performance: Does market structure matter? Journal of Electronic Commerce Research, 11(3), 193–219.
Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185, 1124–1131.
Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4), 297–323.
Ullmann-Margalit, E., & Morgenbesser, S. (1977). Picking and choosing. Social Research, 44(4), 757–785.
Waldeck, R. (2008). Search and price competition. Journal of Economic Behavior and Organization, 66(2), 347–357.
Wan, Y., Menon, S., & Ramaprasad, A. (2009). The paradoxical nature of electronic decision aids on comparison-shopping: The experiments and analysis. Journal of Theoretical and Applied Electronic Commerce Research, 4, 80–96.
Wan, Y., & Peng, G. (2010). What’s next for shopbots? IEEE Computer, 43, 20–26.
Xiao, B., & Benbasat, I. (2007). E-commerce product recommendation agents: Use, characteristics, and impact. MIS Quarterly, 31(1), 137–209.
Xing, X., Yang, Z., & Tang, F. (2006). A comparison of time-varying online price and price dispersion between multichannel and dotcom DVD retailers. Journal of Interactive Marketing, 20(2), 3–20.
Yuan, S. T. (2003). A personalized and integrative comparison-shopping engine and its applications. Decision Support Systems, 34(2), 139–156.
Acknowledgments
A preliminary version of this paper appeared in the Proceedings of the Twenty-Eighth National Conference on Artificial Intelligence (AAAI-2014) [24]. We would like to thank the reviewers of AAAI-2014 for the helpful comments on the earlier version of this paper. This research was partially supported by the ISRAEL SCIENCE FOUNDATION (Grants Nos. 1083/13 and 1488/14) and the ISF-NSFC joint research program (Grant No. 2240/15).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Rights and permissions
About this article
Cite this article
Hajaj, C., Hazon, N. & Sarne, D. Enhancing comparison shopping agents through ordering and gradual information disclosure. Auton Agent Multi-Agent Syst 31, 696–714 (2017). https://doi.org/10.1007/s10458-016-9342-8
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10458-016-9342-8