隨著科技與傳播媒體的蓬勃發展，資訊的傳播越來越方便迅速。消費者在購買產品或服務時，經常能夠獲得豐富的產品訊息，比較由不同賣家提供的產品所能帶給他們的滿足感，這將直接影響消費者效用。然而，相同的產品訊息往往會對不同的消費者會產生不同程度的影響，也就是說揭露產品訊息的影響有好有壞。
根據過去有關訊息揭露的研究，我們整理並發現過往銷售量是影響消費者購買決策的重要因素之一。在我們的研究中，將揭露的訊息定義為賣家前期銷售量，當消費者內心期望的銷售量大於賣家前期實際銷售量時，消費者認為產品低賣，對產品的效用縮減；當消費者內心期望的銷售量小於賣家前期實際銷售量時，消費者認為產品熱賣，對產品的效用膨脹。對於賣家而言，揭露的訊息不會總是好的，也就是說，揭露上期產品的銷量訊息，對下一期消費者的影響因人而異，有部分消費者的效用會膨脹，有部分則會縮減。然而，面對消費者效用變化是不可預期的情況下，為了幫助賣家追求利益最大化，將透過本文的模型協助賣家了解什麼條件下該揭露訊息，什麼條件下不該揭露訊息，可以讓賣家的期望利潤最大化。
在本研究中，將以兩期的購買週期，探討單一賣家面對消費市場時，如何訂定產品售價、生產量、訊息揭露策略，才能讓自身收益最大化。在本文中，我們將考慮兩個案例。在案例I中，賣家不揭露銷售量訊息，消費者購買決策僅取決於賣家兩期賣價。在案例II中，賣家揭露銷售量訊息，消費者購買決策由賣家兩期賣價與揭露訊息的效益所影響。賣家要在銷售的一開始，對第一期的產品售價和生產量進行決策，並在第二期銷售開始前，決定第二期的產品售價，以追求自身利益最大化為目標。對消費者而言，則要決定在第一期購買，還是在第二期才購買，或者完全都不買。

With the vigorous development of technology and communication media, the dissemination of information has become more and more convenient and rapid. When consumers buy products or services, they can often get rich product information and compare the satisfaction that the products provided by different sellers can bring them, which will directly affect consumer utility. However, the same product information will often have varying degrees of impact on different consumers, that is to say, that is to say, the impact of disclosing product information is not always good.
According to past research on information disclosure, we have sorted out and found that the past sales quantity is one of the important factors influencing consumers' purchasing decisions. In our research, the disclosed information is defined as the seller's previous sales quantity. When the consumer's inner expectation of sales is greater than the seller's real sales quantity, the consumer believes that the product is under-selling and the utility of the product is decreased. When the sales quantity expected by the consumer is less than the real sales quantity of the seller in the previous period, the consumer believes that the product is selling well and the utility of the product is increased. For sellers, the disclosed information will not always be good. In other words, disclosure the sales information on the products in the previous period will have a different impact on consumers in the next period. The utility of some consumers will increase, and some will decrease. However, in the face of unpredictable changes in consumer utility. In order to help the seller get maximize profits, the model in this article will help sellers understand under what conditions they should disclose information and under what conditions should not disclose information, so as to maximize the seller's expected profit.
In this study, a two-period purchase will be used to explore how a single seller can set product prices, production quantity, and information disclosure strategies when facing the consumer market in order to maximize their own profits. In this article, we will consider two cases. In case I, the seller does not disclose the sales information, and the consumer's purchase decision only depends on the seller's two-period selling price. In case II, the seller discloses the real sales information, and the consumer's purchase decision is influenced by the seller's two-period selling price and the disclosure information. The seller makes a decision on the product price and production quantity of the period 1 at the beginning of the sale, and decide the product price of the period 2 before the period 2 of sales, with the goal of maximizing their own expected profit. For consumers, they have to decide whether to purchase during the period 1, or in the period 2, or do not purchase at all.

1. Toffler (1970), ‘‘A Future Shock”, Random House. pp. 350-351.
2. William J. Baumol (2002), ‘‘Utility and value’’, Economics & Economic Systems.
3. Jacoby J, Speller DE, Berning CK (1974), “Brand choice behavior as a function of information load: Replication and extension ”, J. Consumer, Res 1, pp. 33-42.
4. Jacoby J (1977), “Information load and decision quality: Some contested issues”, J. Marketing, Res 14, pp. 569-573.
5. Qiang Ye, Zhuo Cheng, Bin Fang (2013), “Learning from other buyers: The effect of purchase history records in online marketplaces”, Decision Support Systems, Vol 56, pp. 502-512.
6. H. Li, Q. Ye, Gajendra S. (2010), “Herding behavior in C2C e-commerce: Empirical investigation in China”, 2010 Internat. Conf. Management Sci, Engrg, pp. 33-39.
7. JH. Huang, YF. Chen (2006), “Herding in online product choice” Psych Marketing, Vol 23, pp. 413-428.
8. Xue (Jane) Tan, Youwei Wang, Yong Tan (2019), “Impact of Live Chat on Purchase in Electronic Markets: The Moderating Role of Information Cues”, Information Systems Research, Vol 30, pp. 1107-1452.
9. Yaxian Gong (2019), “Information quality choice and information disclosure in oligopoly”, Research in Economics, Vol 73, pp. 216-224.
10. Xueming Luo, Siliang Tong, Zheng Fang, Zhe Qu (2019), “Frontiers: Machines vs. Humans: The Impact of Artificial Intelligence Chatbot Disclosure on Customer Purchases”, Marketing Science, Vol 38, pp. 913-1084.
11. Hunt Allcott, Richard L. Sweeney (2016), “The Role of Sales Agents in Information Disclosure: Evidence from a Field Experiment”, Marketing Science, Vol 63, pp. 1-278.
12. Liang Guo (2020), “Upstream Exploitation and Strategic Disclosure”, Marketing Science, Vol 39, pp. 849-1031.
13. Lin Hao, Yong Tan (2018), “Who Wants Consumers to Be Informed? Facilitating Information Disclosure in a Distribution Channel”, Information Systems Research, Vol 30, pp. 1-349.
14. Daniel Bernoulli (1954), “Exposition of a New Theory on the Measurement of Risk”, Econometrica, Vol. 22, pp. 23-36.
15. Ming Hu, Zizhuo Wang, Yinbo Feng (2020), “Information Disclosure and Pricing Policies for Sales of Network Goods” Operations Research, Vol. 68, pp. 965-1284.
16. Fernando Branco, Monic Sun, J. Miguel Villas-Boas (2015), “Too Much Information? Information Provision and Search Costs” Marketing Science, Vol. 35, pp. 539-692.
17. Kalwani, M. U, Yim, C. K, Heikki, J. R, Yoshi, S (1990), “A Price Expectation Model of Customer Brand Choice”, Marketing Research, Vol. 27, pp. 251-262.
18. Lattin, J. M, and Randolph, E. B (1989), “Reference Effects of Price and Promotion on Brand Choice Behavior”, Marketing Research, Vol. 26, pp. 299-310.
19. Putler, D. S (1992), “Incorporating Reference Price Effects into a Theory of Consumer Choice”, Marketing Science, Vol. 11, pp. 287-309.
20. Wilfred Amaldoss, Chuan He (2017), “ Reference-Dependent Utility, Product Variety, and Price Competition” Management Science, Vol. 64, pp. 3971-4470.
21. Kőszegi B, Rabin M (2006), ‘‘A model of reference-dependent preferences”, Quart. J. Econom, Vol. 121, pp. 1133-1165.
22. Heidhues P, Kőszegi B (2008), ‘‘Competition and price variation when consumers are loss averse’’, Amer. Econom. Rev. 98, pp. 1245-1268.