博碩士論文 109423070 詳細資訊




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姓名 劉又德(Yu-Te, Liu)  查詢紙本館藏   畢業系所 資訊管理學系
論文名稱 基於多重代理人機制之現金回饋平台定價策略分析
(Using Multi-Agent Model for Simulating Pricing Strategies in Cashback Platforms)
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檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2028-6-30以後開放)
摘要(中) 隨著網路的普及,人們早已經習慣在網路上進行消費活動,線上購物帶來的便利性以及效率也使得越來越多的消費者開始選擇在網路上購物。自 2019 年COVID-19 爆發,線上購物儼然成為了人們躲避感染風險的主要途徑,這也間接導致傳統零售商被迫轉型為電子商務。

在網路消費的過程中,消費者仍然需要關注商品的質量以及價格是否合理,這也為現金回饋平台和電商平台之間的合作帶來的更多的商機。而現金回饋平台和電商平台則需要在保持競爭的同時確保消費者的權益不受到損害。在這樣的背景下,現金回饋平台、電商平台、消費者,三者之間的利益平衡問題顯得尤為重要。本文將藉由賽局理論分析聯盟行銷中兩種常見的合作方式並透過 Netlogo 進行多代理人模擬現金回饋平台、電商平台和消費者三者之間的定價策略和購買決策,最後比較一般數學模型與代理人模擬兩種方法得出之結果不同之處。

我們發現兩種常見的合作方式中,無論是現金回饋平台亦或是電商平台,導引模式都比傭金模式能夠賺取更高的利潤。消費者對於商品的購買意願由折扣因子而決定,折扣因子越低消費者對低價商品的購買意願越高。而消費者對於商品價值的判定不單純源自於商品定價,很大一部分來自於現金回饋率,現金回饋率越高,即使商品的定價高消費者仍願意購買。同時,折扣因子越低也代表著消費者對於低現金回饋率的容忍度越低。
摘要(英) With the proliferation of the Internet, people have long been accustomed to conducting their shopping activities online. The convenience and efficiency brought by online shopping have led to an increasing number of consumers choosing to shop on the Internet. Since the outbreak of COVID-19 in 2019, online shopping has become the primary avenue for people to avoid the risk of infection, indirectly forcing traditional
retailers to transform into e-commerce businesses.

During the process of online consumption, consumers still need to pay attention to the quality of goods and whether the prices are reasonable. This has created more business opportunities for cashback platforms and e-commerce through their collaboration. However, cashback platforms and e-commerce companies must maintain a competitive edge while ensuring that consumers′ rights are not compromised. In this context, the issue of balancing interests among cashback platforms, e-commerce, and consumers becomes particularly important

This paper aims to analyze two common modes of collaboration using game theory and simulate pricing strategies and purchasing decisions among cashback platforms, e-commerce companies, and consumers through Netlogo agent-based modeling. Finally, a comparison will be made between the results obtained from the general mathematical model and the agent-based simulation to identify any differences

We have found that in the two common modes of collaboration, both cashback platforms and merchant platforms are able to generate higher profits through the lead-based model. The purchasing intention of consumers towards a product is
determined by the discount factor, with a lower discount factor indicating a higher willingness to purchase low-priced products. Moreover, consumers′ evaluation of a product′s value is not only based on its pricing but also heavily influenced by the cashback rate. A higher cashback rate can motivate consumers to make a purchase even if the product′s price is higher. At the same time, a lower discount factor indicates a lower tolerance for low cashback rates among consumers.
關鍵字(中) ★ 賽局理論
★ 定價策略
★ 多代理人
關鍵字(英)
論文目次 目錄
摘要 i
Abstract ii
目錄 iii
圖目錄 iv
表目錄 v
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 3
第二章 文獻探討 4
2.1 聯盟式行銷 4
2.2 優惠券與回饋 4
2.3 代理人機制 5
2.4 Netlogo 6
第三章 研究方法 7
3.1 賽局理論 7
3.2 傭金模式 10
3.3 導引模式 11
第四章 實驗結果 13
4.1.1 傭金模式下的均勻分布 13
4.1.2 傭金模式下的常態分布 16
4.1.3 導引模式下的均勻分布 18
4.1.4 導引模式下的常態分布 21
4.2 消費者折扣因子的影響 23
第五章 結論 32
5.1 研究貢獻 32
參考文獻 33
參考文獻 [1] “Impact of COVID Pandemic on eCommerce,” International Trade Administration.
https://www.trade.gov/impact-covid-pandemic-ecommerce (accessed Mar. 07, 2023).
[2] R. K. Khayru, “Opinions about Consumer Behavior during the Covid-19 Pandemic,”
Journal of Social Science Studies (JOS3), vol. 1, no. 1, pp. 31–36, Jan. 2021.
[3] “Global Cash Back And Rewards App Industry Research Report Competitive Landscape
Market – Market Reports World,”. https://www.marketreportsworld.com/global-cashback-and-rewards-app-industry-research-report-competitive-landscape-market22293536 (accessed Mar. 07, 2023).
[4] “2020 Global Cashback Report highlights industry growth at $108 billion: Identifies 450+
cashback industry leaders, best practices, e-commerce trends, consumer preferences |
Markets Insider,” Jul. 14, 2020.
https://markets.businessinsider.com/news/stocks/2020-global-cashback-reporthighlights-industry-growth-at-108-billion-identifies-450-cashback-industry-leadersbest-practices-e-commerce-trends-consumer-preferences-1029392512 (accessed Mar.
07, 2023).
[5] Y. C. Ho, Y. J. Ho, and Y. Tanc, “Online cash-back shopping: Implications for consumers
and e-businesses,” Information Systems Research, vol. 28, no. 2, pp. 250–264, Jun. 2017.
[6] Donna L. Hoffman and Thomas P. Novak, “How to Acquire Customers on the Web,”
Harvard Business Review, May 2000. https://hbr.org/2000/05/how-to-acquirecustomers-on-the-web (accessed Mar. 14, 2023).
[7] B. Fabian Laurenz Maile, “Artificial Intelligence and Big Data in Affiliate Marketing: A
deep dive into the tools, techniques, and opportunities,” 2018.
[8] Audrey Schomer, “E-Commerce in Digital Media From Insider Intelligence,” INSIDER, Dec.
03, 2018. https://www.businessinsider.com/ecommerce-in-digital-media-report-2018-
11 (accessed Mar. 14, 2023).
[9] Adam Ross, “The Current State And Future Potential Of Affiliate Marketing,” Forbes, Sep.
08,2022.https://www.forbes.com/sites/forbesbusinesscouncil/2022/09/08/thecurrent-state-and-future-potential-of-affiliate-marketing/?sh=37a8820ee0b0
(accessed Jun. 27, 2023).
[10] Philipp Schmitt pschmitt, Bernd Skiera skiera, and Christophe Van den Bulte, “Referral
Programs and Customer Value,” J Mark, vol. 75, no. 1, pp. 46–59, Jan. 2011.
[11] M. T. Akçura, “Affiliated marketing,” Information Systems and e-Business Management,
vol. 8, no. 4, pp. 379–394, Apr. 2010.
[12] S. A. Suryanarayana, D. Sarne, and S. Kraus, “Information Design in Affiliate Marketing,”
Auton Agent Multi Agent Syst, vol. 35, no. 2, pp. 1–28, Oct. 2021.
[13] V. Afonso Vieira, R. Agnihotri, M. I. S. de Almeida, and E. L. Lopes, “How cashback
39
strategies yield financial benefits for retailers: The mediating role of consumers’
program loyalty,” J Bus Res, vol. 141, pp. 200–212, Mar. 2022.
[14] C. Narasimhan, “A Price Discrimination Theory of Coupons,” Marketing Science, vol. 3,
no. 2, pp. 128–147, May 1984.
[15] L. Qiang and S. Moorthy, “Coupons Versus Rebates,” Marketing Science, vol. 26, no. 1,
pp. 67–82, Jan. 2007.
[16] Y. Chen, S. Moorthy, and Z. J. Zhang, “Research Note—Price Discrimination After the
Purchase: Rebates as State-Dependent Discounts,” Manage Sci, vol. 51, no. 7, pp. 1131–
1140, Jul. 2005.
[17] C. Chen and Y. Duan, “Online cash-back shopping with network externalities,” INFOR:
Information Systems and Operational Research , vol. 59, no. 1, pp. 26–52, 2021.
[18] P. Vana, A. Lambrecht, and M. Bertini, “Cashback Is Cash Forward: Delaying a Discount
to Entice Future Spending,” Journal of Marketing Research, vol. 55, no. 6, pp. 852–868,
Dec. 2018.
[19] S. De Marchi and S. E. Page, “Agent-Based Models,” Annual Review, vol. 17, pp. 1–20,
May 2014.
[20] N. Abe and T. Kamba, “A Web marketing system with automatic pricing,” Computer
Networks, vol. 33, no. 1–6, pp. 775–788, Jun. 2000.
[21] Q. Yin, Y. Li, and K. Zhi, “Multi-agent based simulation of negotiate pricing process in
B2C,” Proceedings - 2010 2nd WRI Global Congress on Intelligent Systems, GCIS 2010,
vol. 1, pp. 9–12, 2010.
[22] M. Khouja, M. Hadzikadic, and M. A. Zaffar, “An agent based modeling approach for
determining optimal price-rebate schemes,” Simul Model Pract Theory, vol. 16, no. 1,
pp. 111–126, Jan. 2008.
[23] A. S. Chavez, “Kasbah: An Agent-based Marketplace for Buying and Selling Goods,” 1997.
[24] U. Wilensky, “NetLogo,” 1999. https://ccl.northwestern.edu/netlogo/ (accessed Mar. 18,
2023).
[25] Seymour Papert, Mindstorms. 1980.
[26] Paulo Blikstein, Dor Abrahamson, and Uri Wilensky, “NetLogo: Where We Are, Where
We’re Going,” 2005.
[27] Marc Jaxa-Rozen, Jan H. Kwakkel, and Martin Bloemendal, “A coupled simulation
architecture for agent-based/geohydrological modelling with NetLogo and MODFLOW,”
Environmental Modelling & Software, vol. 115, pp. 19–37, 2019.
[28] Ferdinando Chiacchio, Marzio Pennisi, Giulia Russo, Santo Motta, and Francesco
Pappalardo, “Agent-Based Modeling of the Immune System: NetLogo, a Promising
Framework,” Biomed Res Int, vol. 2014, Aug. 2014.
[29] Jan C. Thiele, Winfried Kurth, and Volker Grimm, “RNETLOGO: an R package for running
and exploring individual-based models implemented in NETLOGO,” Methods Ecol Evol,
40
vol. 3, no. 3, pp. 480–483, Jun. 2012.
[30] Jan C Thiele, “R Marries NetLogo: Introduction to the RNetLogo Package,” J Stat Softw,
vol. 58, no. 2, pp. 1–41, Jun. 2014.
[31] Nathan Hodas, Jacopo Tagliabue, Martin Schmidt, and Jeremy Barofsky, “The Effect of
Leverage on Financial Market,” 2009.
[32] L. Mu, X. Tang, V. Sugumaran, W. Xu, and X. Sun, “Optimal rebate strategy for an online
retailer with a cashback platform: commission-driven or marketing-based?,” Electronic
Commerce Research, 2021.
[33] A. Mehra, S. Kumar, and J. S. Raju, “Competitive Strategies for Brick-and-Mortar Stores
to Counter ‘Showrooming,’” Manage Sci, vol. 64, no. 7, pp. 3076–3090, Jul. 2017.
[34] L. Qiang and S. Moorthy, “Coupons Versus Rebates,” Marketing Science, vol. 26, no. 1,
pp. 67–82, Jan. 2007.
[35] A. T. Coughlan and D. A. Soberman, “Strategic segmentation using outlet malls,”
International Journal of Research in Marketing, vol. 22, no. 1, pp. 61–86, Mar. 2005
指導教授 張李治華 審核日期 2023-6-30
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