This paper focuses on the problem of so-called “lemon markets”, first described by George Akerlof, where information asymmetry about product quality leads to dysfunctional outcomes such as poor average product quality and relatively low levels of trade, resulting in a loss of collective wellbeing. In the age of online commerce, the problem is especially relevant, given that consumers buy many more products without relying on personal experience than at any time in the past. Possible solutions to the problem suggested in the literature are reputation building on the part of producers and improving the information available to consumers, e.g., by way of publicly accessible consumer reviews (even though these can be gamed or faked by sellers). The paper presents the results from a classroom experiment that simulated a “lemon market”. The advantage of using a classroom experiment is that while the market is recreated along a small number of rules and incentives, in line with neat economic models, the participants are real, living decisionmakers, displaying the deviations of actual human behaviour from that of a hypothetical “rational actor”. In all, 294 students majoring in business information technology participated, making up 11 simulated markets. The results presented focus mainly on the supply side, namely, the quantities and prices of goods offered, and nine supply curves are estimated (for three quality grades of goods in three phases of the game). The research concludes that under perfect information, the market performs efficiently. In the condition where only sellers but not buyers have information about product quality, the volume of trade declines, although not as drastically as previous findings have suggested, and the market shows signs of recovery, albeit at a suboptimal equilibrium. After the option of consumer feedback is introduced, the market shows further convergence toward the socially optimal state. The results reaffirm that consumer feedback plays an important role in filling the information gap when product quality is uncertain; however, it is not sufficient in itself to overcome the “lemon market” problem. Other important influences on consumer behaviour under uncertainty are suggested, such as risk-taking, changing attitudes towards the act of (online) purchases, and cultural factors.

1. Akerlof, G. A. (1970). The Market for Lemons: Quality Uncertainty and the Market Mechanism. The Quarterly Journal of Economics, 84(3), 488–500. [Google Scholar] [CrossRef]
2. Akerlof, G., A. (2001). Behavioral macroeconomics and macroeconomic behavior. Nobel Prize Lecture, 8th of December 2001. [Link]
3. Asriyan, V., & Vanasco, V. (2024). Security Design in Non-Exclusive Markets with Asymmetric Information. Review of Economic Studies, 91(2), 690–719. [Google Scholar] [CrossRef]
4. Aumann, R. J. (2019). A synthesis of behavioural and mainstream economics. Nature human behaviour, 3(7), 666–670. [Google Scholar][CrossRef]
5. Baharad, E., & Kliger, D. (2013). Market failure in light of nonexpected utility. Theory and decision, 75, 599–619. [Google Scholar][CrossRef]
6. Bai, J. (2021). Melons as Lemons: Asymmetric Information, Consumer Learning and Seller Reputation. CID Faculty Working Paper Series, (2021), 396, Harvard University, Cambridge, MA [Google Scholar]
7. Barron, O. E., & Qu, H. (2014). Information Asymmetry and the Ex Ante Impact of Public Disclosure Quality on Price Efficiency and the Cost of Capital: Evidence from a Laboratory Market. The Accounting Review, 89(4), 1269–1297. [Google Scholar] [CrossRef]
8. Brauer, J., & Delemeester, G. (2001). Games Economists Play: A Survey of Non-Computerized Classroom-Games for College Economics. Journal of Economic Surveys, 15(2), 221–236. [Google Scholar] [CrossRef]
9. Davidaviciene, V., Raudeliuniene, J., Jonyte-Zemlickiene, A., & Tvaronaviciene, M. (2021). Factors affecting customer buying behavior in online shopping. Marketing and Management of Innovations, 4, 11-19. [Google Scholar] [CrossRef]
10. Duell, N., Steinberg, L., Icenogle, G., Chein, J., Chaudhary, N., Di Giunta, L., Dodge, K. A., Fanti, K. A., Lansford, J. E., Oburu, P., Pastorelli, C., Skinner, A. T., Sorbring, E., Tapanya, S., Uribe Tirado, L. M., Peña Alampay, L., Al-Hassan, S. M., Takash, H. M. S., Bacchini, D., & Chang, L. (2018). Age Patterns in Risk Taking Across The World. Journal of youth and adolescence, 47, 1052–1072.[Google Scholar] [CrossRef]
11. Friedman, M. (2021). Price Theory. Walter de Gruyter GmbH & Co KG.
12. Furnham, A., & Boo, H. C. (2011). A literature review of the anchoring effect. The journal of socioeconomics, 40(1), 35–42. [Google Scholar] [CrossRef]
13. Giannakas, K., & Fulton, M. (2020). On the market for “Lemons”: quality provision in markets with asymmetric information. Humanities and Social Sciences Communications, 7(1), 179. [Google Scholar] [CrossRef]
14. Goldberg, D. M., & Abrahams, A. S. (2022). Sourcing product innovation intelligence from online reviews. Decision Support Systems, 157, 113751. [Google Scholar] [CrossRef]
15. G., Qu, H., & Tarrant, I. (2021). Earnings Forecasts and Price Efficiency after Earnings Realizations: Reduction in Information Asymmetry through Learning from Price. Contemporary Accounting Research, 38(1), 654–675. [Google Scholar] [CrossRef]
16. Henrich, J., Boyd, R., Bowles, S., Camerer, C., Fehr, E., Gintis, H., … & Tracer, D. (2005). “Economic man” in cross-cultural perspective: Behavioral experiments in 15 small-scale societies. Behavioral and brain sciences, 28(6), 795-815. [Google Scholar] [CrossRef]
17. Hofstede, G. J., Jonker, C., Verwaart, T., & Yorke-Smith, N. (2019). The Lemon Car Game Across Cultures: Evidence of Relational Rationality. Group Decision and Negotiation, 28, 849–877. [Google Scholar] [CrossRef]
18. Holt, C. A., & Sherman, R. (1999). Classroom Games: A Market for Lemons. Journal of Economic Perspectives, 13(1), 205–214. [Google Scholar] [CrossRef]
19. Hossain, M. A., Rahman, S., Chowdhury, T. A., Chan, C., Yang, X., & Su, Q. (2018). How signalling mechanisms reduce “lemons” from online group buying (OGB) markets? A study of China. International Journal of Physical Distribution & Logistics Management, 48(7), 658-681. [Google Scholar] [CrossRef]
20. Isaac, R.M., Walker, J.M., & Williams, A.W. (2001). Experimental economics methods in the large undergraduate classroom: Practical considerations. Research in Experimental Economics, 8, 1-23. [Google Scholar] [CrossRef]
21. Jensen, O. W. (1982). Opportunity costs: Their place in the theory and practice of production. Managerial and Decision Economics, 3(1), 48–51. [Google Scholar] [CrossRef]
22. Jones, B. D. (1999). Bounded rationality. Annual review of political science, 2(1), 2973–321. [Google Scholar] [CrossRef]
23. Kahneman, D., Knetsch, J. L., & Thaler, R. (1986). Fairness as a Constraint on Profit Seeking: Entitlements in the Market. The American Economic Review, 76(4), 728–741. [Google Scholar] [CrossRef]
24. Kolnhofer-Derecskei, A. (2020). How Much Might a Beer Cost in a Fancy Resort? A Possible Replication of Thaler’s Well-Known Experiment. Perspectives on Consumer Behaviour: Theoretical Aspects and Practical Applications, 241-257. [Google Scholar] [CrossRef]
25. Kolnhofer Derecskei, A., & Csongradi, G. (2022). How do the framing effects, environmental factors and personal risk perceptions influence our decision about a hypothetical COVID-19 pill?. Journal of Decision Systems, 31(sup1), 131-152. [Google Scholar] [CrossRef]
26. Kolnhofer-Derecskei, A., Csongradi, G., Tóth, A., & Hauber, G. (2022). Hypothetical demand curve and price for the COVID-19 vaccine. Forum Scientiae Oeconomia, 4, 175–197. [Google Scholar] [CrossRef]
27. Krishnan, C., Agarwal, N., & Gupta, S (2022). Factors Influencing Consumer Purchase Intention of Luxury Branded Perfumes. Marketing and Management of Innovations, 3, 190–199. [Google Scholar] [CrossRef]
28. Li, T. J., Cao, K., Liu, Z.-C., Yuan, Z.-Y., Li, P. J., & Li, Y. (2022). Linking Consumer’s Needs with Product Performance on E-shopping Platforms with Fashion Big Data (FBD) API Plugins. In 15^th Textile Bioengineering and Informatics Symposium TBIS 2022, 205–215.[Google Scholar]
29. Light, D. W., & Lexchin, J. R. (2021). Pharmaceuticals as a market for “lemons”: Theory and practice. Social Science & Medicine, (268), 113368. [Google Scholar] [CrossRef]
30. Lin, Y. (2012). Game theory research on the ‘lemon’ problem of the e-commerce market. In Proceedings of the 2012 2nd International Conference on Computer and Information Application (ICCIA 2012). Paris, France: Atlantis Press. [Google Scholar] [CrossRef]
31. Mamada, R. (2022). The market for lemons and information theory. Mathematical Social Sciences, (120), 107–112. [Google Scholar][CrossRef]
32. Mandl, C. E. (2023). Information Asymmetry: When Markets Fail. In: Managing Complexity in Social Systems. Management for Professionals. Springer, Cham. [Google Scholar] [CrossRef]
33. Mankiw, G., N. (2017). Principles of Microeconomics. 8th Edition, Cengage. [Google Scholar]
34. Meskic, Z., Albakjaji, M., Omerovic, E., & Alhussein, H. (2022). Transnational consumer protection in E-commerce: Lessons learned from the European Union and the United States. International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), 13(1), 1–15. [Google Scholar] [CrossRef]
35. Muthoo, A., & Mutuswami, S. (2011). Imperfect Competition and Efficiency in Lemons Markets. Economic Journal, 121(552), 580–594. [Google Scholar] [CrossRef]
36. Our World in Data (n.d.). Trust. [Link]
37. Pei, Z., & Paswan, A. (2018). Consumers’ legitimate and opportunistic product return behaviors in online shopping. Journal of Electronic Commerce Research, 19(4), 301–319. [Google Scholar] [CrossRef]
38. Praveen, S. V., Gajjar, P., Ray, R. K., & Dutt, A. (2024). Crafting clarity: Leveraging large language models to decode consumer reviews. Journal of Retailing and Consumer Services, 81, 103975. [Google Scholar] [CrossRef]
39. Qi, L., & Xianfeng, Z. (2003). Lemon Problems in the internet Transactions and Relative Strategies. In Current Security Management & Ethical Issues of Information Technology (pp. 236–248). IGI Global. [Google Scholar] [CrossRef]
40. Rolison, J. J., Hanoch, Y., Wood, S., & Liu, P. J. (2014). Risk-taking differences across the adult life span: a question of age and domain. Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 69(6), 870–880. [Google Scholar] [CrossRef]
41. Salmi, M. (2022). An attempt at a literature review of ‘The Market for Lemons’ theory and a case study in Algerian local e-commerce. Munich Personal RePEc Archive. [Link]
42. Samuelson, P., & Nordhaus, W. (1985) Economics. 12th Edition, McGraw-Hill, New York.
43. Selten, R., & Stoecker, R. (1986). End behaviour in sequences of finite prisoner’s dilemma supergames. Journal of Economic Behavior & Organization, 7(1), 47–70. [Google Scholar] [CrossRef]
44. Shivaprasad, T. K., & Shetty, J. (2017). Sentiment analysis of product reviews: A review. Proceedings of the International Conference on Inventive Communication and Computational Technologies, ICICCT 2017, 298–303. [Google Scholar] [CrossRef]
45. Smith, V. (1989). Theory, experiment and economics. The journal of economic perspectives, 3(1), 151-169. [Google Scholar] [CrossRef]
46. Staveley-O’Carroll, J., & Gai, Y. (2023) Adverse selection and risk pooling in the health insurance market: A classroom demonstration. The Journal of Economic Education, 54(3), 256–266. [Google Scholar] [CrossRef]
47. Thaler, R. (1980). Toward a positive theory of consumer choice. Journal of Economic Behavior & Organization, 1(1), 39–60. [Google Scholar] [CrossRef]
48. Thaler, R. (1985). Mental accounting and consumer choice. Marketing Science, 4(3), 199-214. [Google Scholar] [CrossRef].
49. Thierer, A. D., Koopman, C., Hobson, A., & Kuiper, C. (2015). How the internet, the Sharing Economy, and Reputational Feedback Mechanisms Solve the “Lemons Problem”. SSRN Electronic Journal. [Google Scholar] [CrossRef]
50. Tomer, J. F. (2007). What is behavioral economics?. The Journal of Socio-Economics, 36(3), 463–479. [Google Scholar] [CrossRef]
51. Tsao, H.-Y., Campbell, C., Farshid, M., & Chakrabarti, R. (2009). Lemon-aid : brand as a signal for quality – a classroom game. Proceedings: ANZMAC Annual Conference. Presented at the Australian and New Zealand Marketing Academy Conference. [Link]
52. Tversky, A., & Kahneman, D. (1992). Advances in Prospect Theory: Cumulative Representation of Uncertainty. Journal of Risk and Uncertainty, 5(4), 297–323. [Google Scholar] [CrossRef]
53. Wolf, J. R., & Myerscough, M. A. (2007). Reputations in Markets with Asymmetric Information: A Classroom Game. The Journal of Economic Education, 38(4), 393–405. [Google Scholar] [CrossRef]
54. Woods, D. W., & Moore, T. (2020). Cyber Warranties: Market Fix or Marketing Trick? Communications of the ACM, 63(4). [Google Scholar] [CrossRef]
55. Wu, Y., Ngai, E. W., Wu, P., & Wu. C. (2020). Fake online reviews: Literature review, synthesis, and directions for future research. Decision Support Systems, 132, [Google Scholar] [CrossRef]
56. Zhang, C., & Tian, Y.-X. (2024). Joint optimization of dynamic pricing, quality, and production quantity in the presence of online reviews: A distributionally robust optimization approach. Computers and Operations Research, [Google Scholar] [CrossRef]