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研究生:彭晴
研究生(外文):PENG, CHING
論文名稱:以集群分析探討電商平台資料特性:以蝦皮購物I公司為例
論文名稱(外文):Exploring the data characteristics of e-commerce platforms through cluster analysis: Case study on I company in Shopee
指導教授:林鴻文林鴻文引用關係
指導教授(外文):LIN, HONG-WEN
口試委員:謝艾芸詹雅晴
口試委員(外文):HSIEH, AI-YUNJHAN, YA-CHING
口試日期:2023-12-28
學位類別:碩士
校院名稱:中國文化大學
系所名稱:電子商務碩士學位學程
學門:商業及管理學門
學類:一般商業學類
論文種類:學術論文
論文出版年:2024
畢業學年度:112
語文別:中文
論文頁數:47
中文關鍵詞:電子商務大數據資料探勘集群分析新冠肺炎
外文關鍵詞:E-commerceBig DataData MiningCluster AnalysisCOVID-19
相關次數:
  • 被引用被引用:1
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  • 評分評分:
  • 下載下載:65
  • 收藏至我的研究室書目清單書目收藏:0
科技的進步促使網路逐漸普及化,也塑造出一個不同於以往的新興消費模式—電子商務。2020年新冠肺炎在全球各地蔓延逐漸造成大流行,人們因為疫情不得不改變原本的生活模式,也因此將電子商務的發展推上了另一個新高峰,並且被看好其未來市場需求仍會持續成長。電子商務為企業開拓了更多可能性,因此若懂得掌握電子商務之特性並善加利用勢必能讓企業保持優勢地位。本研究以蝦皮購物商城商家為個案研究對象,並蒐集2020年7月至2023年2月之間共計973筆的每日交易資料作為研究數據,欲探討疫情間消費者之線上消費行為。為了找出蝦皮購物商城商家疫情期間每日交易資料的異同之處,本研究使用資料探勘中的 K-means集群分析法對其進行分析,研究結果得出共6個不同特徵之集群,依序為表現良好群、表現低落群、表現穩定群、表現有潛力群、表現優異群、表現特殊群,最後並依據各集群結果,於結論部分提供建議予品牌企業端作為擬訂行銷經營策略之參考,同時提出未來研究方向,後續研究者可以此進行不同角度的深入探討。
The advance of technology has led to the gradual popularization of the Internet and shaped a new emerging business model, namely e-commerce. The COVID-19 pandemic, which spread globally in 2020, has forced people to change their lifestyles and consequently propelled the development of e-commerce to new heights. Moreover, the future market demand for e-commerce is expected to continue growing. E-commerce opens up more possibilities for businesses and understanding its characteristics and leveraging them effectively can undoubtedly help enterprises maintain a competitive advantage. This study focuses on the Shopee sellers as the case study subjects. A total of 973 daily transaction data from July 2020 to February 2023 were collected as research data to investigate consumer online shopping behavior during the pandemic. To identify the similarities and differences in daily transaction data of Shopee sellers during the pandemic. This study utilizes K-Means clustering for data mining to conduct the analysis. The research findings reveal six distinct clusters with different characteristics: High-Performance Cluster, Low-Performance Cluster, Stable Performance Cluster, Potential Performance Cluster, Outstanding Performance Cluster, and Special Performance Cluster. Finally, based on the results of each cluster, recommendations are provided in the conclusion for brand enterprises to formulate marketing and business strategies. Additionally, future research directions are proposed to encourage further exploration from different perspectives by subsequent researchers.
中文摘要 iii
英文摘要 iv
誌謝辭 v
內容目錄 vi
表目錄 viii
圖目錄 i�x
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 2
第三節 研究流程 3
第二章 文獻探討 5
第一節 電子商務之定義 5
第二節 DIKW金字塔 7
第三節 數據分析與應用 9
第三章 研究方法 16
第一節 研究對象 16
第二節 集群變數 17
第三節 K-means集群分析法 19
第四章 研究結果 21
第一節 集群數依據 21
第二節 集群結果 23
第五章 結論與建議 25
第一節 結論 25
第二節 管理意涵 26
第三節 研究限制與建議 29
參考文獻 30
附錄 38


一、 中文部分

【零售電商消費者調查系列一】6成網友愛用蝦皮24h、momo 行動App購物冠軍為蝦皮 10大網購數位科技 消費者最重視電子支付、跨平台比價(2022, May 12),MIC資策會產業情報研究所,來源:https://mic.iii.org.tw/news.aspx?id=621

2011中華民國電子商務年鑑,中華民國經濟部,來源:
https://www.moea.gov.tw/Mns/populace/publication/Publication.aspx?menu_id=149&pub_id=5022 [2011, December 31]

Ronald, S. S. (2001). 深化顧客關係管理 (賴士奇, Trans.; 初版). 遠擎管理顧問有限公司.

陳浩寧(2021, November 26),IMA白皮書調查,台灣兩千大企業數位轉型成熟度約6.3分,明年投資額增加,YAHOO新聞,來源:https://tw.news.yahoo.com/news/ima%E7%99%BD% E7%9A%AE%E6%9B%B8%E8%AA%BF%E6%9F%A5-% E5%8F%B0%E7%81%A3%E5%85%A9%E5%8D%83%E5% A4%A7 %E4%BC%81%E6%A5%AD%E6%95%B8%E4%BD %8D%E8%BD%89%E5%9E%8B%E6%88%90%E7%86%9F %E5%BA%A6%E7%B4%846-3%E5%88%86-%E6%98%8E% E5%B9%B4%E6%8A%95%E8%B3%87%E9%A1%8D%E5% A2 %9E%E5%8A%A0-083629639.html

鄭婉儀(2001),應用資料探勘於交叉銷售之研究,國立台北大學企業管理研究所未出版之碩士論文

諶家蘭(2015),企業導入大數據分析與應用之概述,會計研究月刊,355,54–58


二、 英文部分

(2022). Statista. Available:https://www.statista.com/

Adriaans, P. W., & Zantinge. (1996). Data Mining. Addison-Wesley.

Afuah, A., & Tucci, C. L. (2003). A Model of the Internet as Creative Destroyer. IEEE Transactions on Engineering Management, 50(4), 395–402.

Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, S. J. (2016). How to Improve Firm Performance Using Big Data Analytics Capability and Business Strategy Alignment? International Journal of Production Economics, 182, 113–131.

Alon, I., Farrell, M., & Li, S. (2020). Regime Type and COVID-19 Response. FIIB Business Review, 9(3) , 152–160.
https://doi.org/https://doi.org/10.1177/2319714520928884

Amazon.com Announces Fourth Quarter Results. (2023). Businesswire. Available:https://www.businesswire.com/news/home/20230201005991/en/Amazon.com-Announces-Fourth-Quarter-Results [Feb 02, 2023]

Balakrishnam, P. V. (1994). A Study of the Classification Capabilities of Neural Networks Using Unsupervised Learning - A Comparison K-Means Clustering. Psychomertrika, 59, 509–525.

Berry, A. J. M., & Linoff, G. (1997). Data Mining Techniques for Marketing , Sales, and Customer Support. John Wiley&Sons Inc.

Berson, Smith, A. S., & Thearling, K. (2000). Building Data Mining Application for CRM.

Chen, M. S, Han, J., & Yu, P. S. (1996). Data Mining: An Overview from a Database Perspective. IEEE Transactions on Knowledge and Data Engineering, 8(6), 866–883. https://doi.org/http://dx.doi.org/10.1109/69.553155

Chevalier, Stephanie. (2022). Global retail e-commerce sales 2014-2026.Statista. Available:https://www.statista.com/statistics/379046/worldwide-retail-e-commerce-sales/ [Sep 21, 2022]

Chye, K. H., & Gerry, C. K. L. (2002). Data Mining and Customer Relationship Marketing in the Banking Industry. Singapore Management Review, 24, 1–27.

Cios, K. J., & Moore, G. W. (2002). Uniqueness of Medical Data Mining. Artif Intell Med, 26(1–2), 1–24.

Davis, C. K. (2014). Beyond Data and Analysis. Communications of the ACM, 57(6), 39–41.

Dixon, T., & Marston, A. (2002). U.K. Retail Real Estate and the Effects of Online Shopping. Journal of Urban Technology, 9(3), 19–47.

Ferraris, A., Mazzoleni, A., Devalle, A., & Couturier, J. (2018). Big Data Analytics Capabilities and Knowledge Management: Impact on Firm Performance. Journal of Management Decision, 57(8), 1923–1936.

Fisher, M., & Raman, A. (2018). Using Data and Big Data in Retailing. Production and Operations Management, 27(9), 1665–1669.

Fu, Y. (1997). Data Mining Task, Technique and Applications. IEEE POTENTIALS.

Ghasemaghaei, M., Hassanein, K., & Turel, O. (2015). Impacts of Big Data Analytics on Organizations: A Resource Fit Perspective.

Global E-Commerce Outlook. (2021). CBRE. Available:https://www.cbre.com/insights/reports/global-e-commerce-outlook-2021

Goepfert, G. (2021, August 17). European Big Data Spending Will Reach $50 Billion This Year, as Companies Focus on Analytics-Enabled Hyper-Automation, Says IDC. IDC Media Center. Available:https://www.idc.com/getdoc.jsp?containerId=prUS48165721

Hallikainen, H., Savimäki, E., & Laukkanen, T. (2020). Fostering B2B sales with customer big data analytics. Industrial Marketing Management, 86, 90-98.

Hamzah, A., Yazid, M. F. M, & Shamsudin, M. F. (2020). Post Covid-19: What Next for Real Estate Industrial Sector in Malaysia? Journal of Postgraduate Current Business Research, 1(1), 3–6.

Han, J., & Kamber, M. (2001). Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers.

Hashem, T. N. (2020). Examining the Influence of COVID-19 Pandemic in Changing Customers’ Orientation towards E-Shopping. Modern Applied Science, 14(8), 59–76. https://doi.org/10.5539/mas.v14n8p59

Henrion, M. (2021). Why most big data analytics projects fail. How to succeed by engaging with your clients.

Hyun, Y., Kamioka, T., & Hosoya, R. (2020). Improving Agility Using Big Data Analytics: The Role of Democratization Culture. Pacific Asia Journal of the Association for Information Systems, 12(2), 2.

Jung, J. J. (2011). Service Chain-Based Business Alliance Formation in Service-Oriented Architecture. Expert Systems with Applications, 38(3), 2206–2211. https://doi.org/http://dx.doi.org/10.1016/j.eswa.2010.08.008

Kalakota, R., & Robinson, M. (2001). E-business 2.0: Roadmap for success. Addison-Wesley Professional.

Kalakota, R., & Whinston, A. B. (1997). Electronic Commerce: A Manager’s Guide. (2nd ed.). Addison-Wesley Professional.

Khanra, S., Dhir, A., & Mäntymäki, M. (2020). Big data analytics and enterprises: a bibliometric synthesis of the literature. Enterprise Information Systems, 14(6), 737-768.

Kim, R. Y. (2020). The Impact of COVID-19 on Consumers: Preparing for Digital Sales. IEEE Engineering Management Review, 48(3), 212–218.

Korper, S., & Ellis, J. (2000). The E-Commerce Book: Building the E-Empire. Academic Press.

Lissitsa, S., & Kol, O. (2016). Generation X vs. Generation Y–A Decade of Online Shopping.Journal of Retailing and Consumer Services, 31,304–312. https://doi.org/10.1016/j.jretconser.2016.04.015

Loebbecke, C., & Picot, A. (2015). Reflections on Societal and Business Model Transformation Arising from Digitization and Big Data Analytics: A Research Agenda. The Journal of Strategic Information Systems, 24(3), 149–157.

Mcafee, J., & Brynjolfsson, E. (2012). Big Data: The Management Revolution. Harvard Business Review, 90(10), 60–68.

Mikalef, P., Krogstie, J., Pappas, I. O., & Pavlou, P. (2020). Exploring the Relationship between Big Data Analytics Capability and Competitive Performance: The Mediating Roles of Dynamic and Operational Capabilities. Information & Management, 57(2), 103–169.

Mikalef, P., Pappas, I. O., Krogstie, J., & Giannakos, M. (2018). Big data analytics capabilities: a systematic literature review and research agenda. Information Systems and e-Business Management, 16(3), 547-578.

Mikalefa, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). Big Data Analytics and Firm Performance: Findings from a Mixed-Method Approach. Journal of Business Research, 98, 261–276.

Morimura, F., & Sakagawa, Y. (2023). The Intermediating Role of Big Data Analytics Capability between Responsive and Proactive Market Orientations and Firm Performance in the Retail Industry. Journal of Retailing and Consumer Services, 71, 103–193. https://doi.org/10.1016/j.jretconser.2022.103193

Müller, O., Junglas, I., Vom Brocke, J., & Debortoli, S. (2016). Utilizing big data analytics for information systems research: challenges, promises and guidelines. European Journal of Information Systems, 25(4), 289-302.

Nagra, G., & Gopal, R. (2013). An Study of Factors Affecting on Online Shopping Behavior of Consumers. International Journal of Scientific and Research Publications, 3(6), 1–4.

Nemat, R. (2011). Taking a Look at Different Types of E-Commerce. World Applied Programming, 1(2), 100–104.

Olaru, C., Wehenkel, L., & Geurts, P. (1999). Data Mining Tools and Application in Power System Engineering. Proceedings of the 13th Power System Computation Conference, PSCC99, 324–330.

Peacock, P. R. (1998). Data Mining in Marketing: Part 1. Marketing Management, 6, 8–18.

Rabhi, L., Falih, N., Afraites, A., & Bouikhalene, B. (2019). Big Data Approach and Its Applications in Various Fields. Procedia Computer Science, 155, 599–605.

Rathi, M. S. R., & Bora, C. (2020). Challenges Before E-Commerce in COVID-19. Mukt Shabd Journal, 34(33), 103–112.

Rayport, J. F., & Jaworski, B. J. (2001). Cases in E-Commerce. McGraw-Hill Higher Education.

San, O.M., Huynh, V., and Nakamori, Y. (2004), An alternative extension of the K-means algorithm for clustering categorical data, Int. J. Appl. Math. Comput. Sci., 14(2), 241-247.

Simoudis, E. (1996). Reality Check for Data Mining. IEEE Intelligent Systems, 11(5), 26–33.

Stanford-smith, B., & Kidd, P. T. (2000). E-Business: Key Issues, Applications and Technologies. IOS Press.

Tarhini, A., Alalwan, A. A., Al-qirim, N., & Algharabat, R. (2018). An Analysis of the Factors Influencing the Adoption of Online Shopping. International Journal of Technology Diffusion (IJTD), 9(3), 68–87. https://doi.org/10.4018/IJTD.2018070105

Vassakis, K., Petrakis, E., & Kopanakis, I. (2018). Big data analytics: applications, prospects and challenges. In Mobile big data (pp. 3-20). Springer, Cham.

Wang, G., Gunasekaran, A., Ngai, E. W., & Papadopoulos, T. (2016). Big Data Analytics in Logistics and Supply Chain Management: Certain Investigations for Research and Applications. International Journal of Production Economics, 176, 98–110.

Wang, Y. J., Minor, M. S., & Wei, J. (2011). Aesthetics and the Online Shopping Environment: Understanding Consumer Responses. Journal of Retailing, 87(1), 46–58. https://doi.org/10.1016/j.jretai.2010.09.002

World Trade Organisation Joint Statement Initiative on Electronic Commerce. (2020, November 13). Government of Cananda. Available:https://www.international.gc.ca/world-monde/international_relations-relations_internationales/wto-omc/electronic-commerce-electronique.aspx?lang=eng
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