雙重線上通路之不同服務情境下的供應商定價決策

Supplier’s Pricing Decisions under Different Service Scenarios in Dual Online Channels

藍千慈(Cian-Cih Lan)

指導教授 ： 周雍強

國立臺灣大學/工學院/工業工程學研究所/碩士(2019年)

https://doi.org/10.6342/NTU201902433

摘要

隨著時代演進，愈來愈多經營品牌的供應商不僅開啟了線上通路，也選擇了多通路策略。與過去雙重通路文獻不同的是，本研究以雙重線上通路－直接通路、平台通路作為研究範圍，且兩個通路的定價者皆為供應商。因為供應商總收益來自兩個通路，所以不同通路須在競爭關係之下共存，供應商如何在電商平台採用兩部收費契約獲取部分收益的情況下，為不同通路進行定價進而最大化收益，各項參數又是如何影響供應商，是本研究的第一個部分。
而服務在現今社會中愈顯重要，當供應商試圖推出新服務於直接通路來達到特定目的時，是否能察覺此舉帶來的改變便成了一個問題。本研究的第二部分，欲藉由貝式學習來追蹤特定參數，進而了解提供服務對消費者產生的影響，並以此作為重新定價的依據，達到動態決策的效果。

關鍵字

雙重線上通路 ； 兩部收費契約 ； 定價 ； 服務價值 ； 貝式學習

並列摘要

An increasing number of branding suppliers have developed online channels and adopted the multi-channel strategy. Different from previous studies on traditional dual-channel systems, this study focused on online dual-channel systems, namely direct channel and business to business to consumer (B2B2C) channel, and the product prices on these two channels are set by suppliers. Because the suppliers earn their total revenue from the two channels, the channels have to coexist under a competitive relationship. Therefore, this study first investigated how suppliers price their products in different channels to maximize their revenue when e-commerce platforms use two-part tariff to obtain partial revenue, as well as how various parameters influence the suppliers.
Service has been increasingly crucial in today’s society. When suppliers intend to launch new services in direct channels to achieve a specific goal, whether they are aware of the resulting changes is a question worthy of investigation. Accordingly, the second part of this study used Bayesian learning to track certain parameters to understand the effect of service on consumers. The result was then used as the basis for repricing to achieve dynamic decision-making.

並列關鍵字

dual online channel ； two-part tariff ； pricing ； service value ； Bayesian learning

參考文獻

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