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研究生: 邱振瑜
Chiu, Chen-Yu
論文名稱: 基於外送平台服務下之業者定價模式探討
Investigate store pricing model based on delivery platform service
指導教授: 劉書助
Liu, Shu-Chu
學位類別: 碩士
Master
系所名稱: 管理學院 - 資訊管理系所
Department of Management Information Systems
畢業學年度: 109
語文別: 中文
論文頁數: 33
中文關鍵詞: 外送平台服務粒子群演算法定價模式
外文關鍵詞: Delivery platform, Particle Swarm Optimization, Pricing strategy
DOI URL: http://doi.org/10.6346/NPUST202100292
相關次數: 點閱:12下載:0
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  • 外送平台因受疫情影響,導致需求量增大。業者與外送平台合作時需要給予抽成費用,導致業者面臨定價是否該漲價的抉擇。雖然顧客對定價是非常敏感,但是業者也必須考慮到成本。現今有許多種定價方法,本研究以顧客為導向探討定價模式,以獲得較高的平均利潤與顧客滿意度。
    本研究以粒子群演算法為基礎,利用問卷資料所蒐集到的顧客接受價格透過模擬程式產生平均利潤與顧客滿意度。最終得出最佳組合解,為業者提高利潤與顧客滿意度。研究顯示在粒子群演算法的定價下優於業者定價,另考慮針對不同抽成比率和訂單間隔的一些敏感性分析。抽成比率增加,導致定價上升、平均利潤下降,顧客滿意度下降;訂單間隔增加,導致平均利潤下降,顧客滿意度上升。

    Delivery platforms are affected by the outbreak, resulting in increased demand. Although customers are very sensitive to pricing, operators must also consider the cost. There are many kinds of pricing methods nowadays, and this study is customer-oriented to discuss pricing models.
    This research is based on Particle Swarm Optimization, using the customer acceptance price collected from the questionnaire to generate average profit and customer satisfaction through a simulation program. Finally, the best combination solution can be obtained to improve profits and customer satisfaction for the industry. Research shows that the pricing of PSO algorithm is better than the pricing of the industry. In addition, some sensitivity analysis for different commission ratios and order intervals is considered. The increase in the commission ratio leads to an increase in pricing, a decrease in average profits, and a decrease in customer satisfaction; an increase in order intervals leads to a decrease in average profits and an increase in customer satisfaction.

    摘要 I
    Abstract II
    目錄 III
    表目錄 V
    圖目錄 VI
    第1章、緒論 1
    1.1研究背景與動機1
    1.2研究目的 3
    1.3研究流程 4
    1.4研究範圍 6
    第2章、文獻探討 7
    2.1定價 7
    2.2粒子群演算法 8
    第3章、研究方法 12
    3.1建置粒子群演算法 14
    第4章、模擬驗證 19
    4.1個案探討 19
    4.2建置環境 19
    4.3參數設定 19
    4.4實驗結果分析 21
    4.5敏感度分析 23
    第5章、結論 27
    5.1結論 27
    5.2研究限制與建議 28
    參考文獻 30

    附錄 33
    附錄一 顧客調查問卷 33

    中文文獻
    [1]白凢芸、葉子明、蕭鈺錦(2020)。第三方餐飲外送平台價值分析:餐飲供應合作夥伴之觀點。電子商務學報,22(2),213-238。

    [2]林南宏、曾家渝(2018)。顧客導向和銷售導向對滿意度,信任及顧客關係績效的影響-銀行業的實證結果,北商學報,(34),25-57。

    [3]蒙卉薇、孫雨熙(譯)(2018)。精準訂價:在商戰中跳脫競爭的獲利策略(原作者:Hermann Simon)。台北市:天下雜誌。(原著出版年:2015)

    [4]宋裕祺、王俊穎(2015)。結合粒子群演算法與遺傳演算法於斜張橋鋼索預力之最佳化設計。中國土木水利工程學刊,27(1),1-10。

    [5]張景晴、林宇銜(2015)。結合粒子群演算法及大渦模擬進行橢圓柱排列隊形最佳化之研究。中國造船暨輪機工程學刊,34(3),155-163。

    [6]施由宜(2013)。供應商調適之多構面模式-創造客戶價值之基礎,商管科技季刊,14(2),217-242。

    [7]蔡翼擎、謝涵聿、呂蕙竹、陳貞錡、施嘉慧、歐德威(2020)。探討台灣餐飲外送平台如何建構競爭優勢之關鍵決策因子。管理資訊計算,9(1),109-122。

    [8]林南宏、曾家渝(2018)。顧客導向和銷售導向對滿意度,信任及顧客關係績效的影響-銀行業的實證結果。北商學報,(34),25-57。

    英文文獻
    [1]Arnold, B. C. (2014). Pareto distribution. Wiley StatsRef: Statistics Reference Online (pp.1-10). doi:10.1002/9781118445112

    [2]Bansal, J. C. (2019). Particle swarm optimization. In Evolutionary and swarm intelligence algorithms (pp. 11-23). Springer, Cham.

    [3]Cannon, H. M., & Morgan, F. W. (1990). A strategic pricing framework. Journal of Services Marketing (Vol. 4 No. 2, pp. 19-30).
    doi: /10.1108/EUM0000000002508

    [4]El-Adly, M. I. (2019). Modelling the relationship between hotel perceived value, customer satisfaction, and customer loyalty. Journal of Retailing and Consumer Services, 50, 322-332.

    [5]Ingenbleek, P. (2014). The theoretical foundations of value-informed pricing in the service-dominant logic of marketing. Management Decision, 52(1), 33-53.

    [6]Kortge, G. D., & Okonkwo, P. A. (1993). Perceived value approach to pricing. Industrial Marketing Management, 22(2), 133-140.

    [7]Kienzler, M. (2018). Value-based pricing and cognitive biases: An overview for business markets. Industrial Marketing Management, 68, 86-94.

    [8]Niu, B., Li, Q., Mu, Z., Chen, L., & Ji, P. (2021). Platform logistics or self-logistics? Restaurants’ cooperation with online food-delivery platform considering profitability and sustainability. International Journal of Production Economics, 234, 108064.

    [9]Shi, Y., & Eberhart, R. (1998, May). A modified particle swarm optimizer. In 1998 IEEE international conference on evolutionary computation proceedings. IEEE world congress on computational intelligence (Cat. No. 98TH8360) (pp. 69-73). IEEE.

    [10]Tian, D., & Shi, Z. (2018). MPSO: Modified particle swarm optimization and its applications. Swarm and Evolutionary Computation, 41, 49-68.

    [11]Vitorino, L. N., Ribeiro, S. F., & Bastos-Filho, C. J. (2015). A mechanism based on artificial bee colony to generate diversity in particle swarm optimization. Neurocomputing, 148, 39-45.

    [12]Wang, D., Tan, D., & Liu, L. (2018). Particle swarm optimization algorithm: an overview. Soft Computing, 22(2), 387-408.

    [13]Waluyo, A., Jatnika, H., Permatasari, M. R. S., Tuslaela, T., Purnamasari, I., & Windarto, A. P. (2020, June). Data Mining Optimization uses C4. 5 Classification and Particle Swarm Optimization (PSO) in the location selection of Student Boardinghouses. In IOP Conference Series: Materials Science and Engineering (Vol. 874, No. 1, p. 012024). IOP Publishing.

    [14]Wu, Z., Kazaz, B., Webster, S., & Yang, K. K. (2012). Ordering, pricing, and lead‐time quotation under lead‐time and demand uncertainty. Production and Operations Management, 21(3), 576-589.

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