摘要(英) |
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. |
參考文獻 |
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. |