以機器學習技術建立先買後付消費者族群預測模型：以臺灣電子商務購物網站為例

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張家薇(Chia-Wei Chang) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

以機器學習技術建立先買後付消費者族群預測模型：以臺灣電子商務購物網站為例
(Building a Predictive Model for Buy Now, Pay Later Consumer Segments Using Data Mining Techniques: A Case Study of Taiwan E-commerce Website)

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至系統瀏覽論文 (2029-7-1以後開放)

摘要(中)

在疫情蔓延全球期間，由於各地區實施封鎖措施部分實體店面被迫關閉，使得消費者的購物方式迅速從線下轉向線上，這種轉變加速了電子商務的發展，也促使消費者支付方式的改變，其中先買後付(Buy Now Pay Later)服務快速崛起。先買後付為消費者提供了可遞延或分期付款的支付方式，消費者無需即時支付全額，降低消費者經濟壓力，同時增加購買意願。特別是在經濟不確定的後疫情時代，此外，這種付款方式在心理上也為消費者減輕支付時的壓力。
由於先買後付服務在全球迅速普及，對電子商務平台而言，能夠準確識別和預測潛在使用此服務的消費者並推播其使用，期能藉此降低消費者支付透明度以提升購物衝動，有助於提升消費者購買意願、消費金額及消費者整體滿意度，故本研究以機器學習技術建立先買後付消費者預測模型，以提供電商平台更有效地制定行銷策略和精準推廣增加服務使用率。
以電子商務平台消費者交易資料，包括購買頻率、金額、商品偏好及支付偏好等，鑑於原始資料集中存在的資料不平衡問題，本研究採用了六種資料不平衡技術，如隨機過採樣、隨機欠採樣、SMOTE、ADASYN、ENN及SMOTE+ENN技術，以提高模型對少數樣本的預測能力。在模型建立階段，選擇搭配六種機器學習，包括邏輯迴歸、隨機森林、梯度提升技術、KNN、樸素貝葉斯、神經網路進行模型建立，並採用十折交叉驗證方法來評估各個模型的結果。研究結果顯示，比較不同資料不平衡及機器學習模型，資料不平衡經SMOTE+ENN處理並應用梯度提升技術，模型在整體預測表現及穩定性結果最佳。

摘要(英)

During the pandemic, lockdown measures forced many stores to close, rapidly shifting consumer shopping from offline to online. This shift not only accelerated the development of e-commerce but also led to changes in consumer payment methods. Buy Now, Pay Later service emerged rapidly, offering consumers a way to defer or installment payments. This payment method does not require consumers to pay the full amount immediately, reducing their financial stress and increasing their willingness to purchase, especially in the economically uncertain post-pandemic era. Additionally, this payment method psychologically reduces the stress of payment at the time of purchase, thereby enhancing the shopping experience and consumer satisfaction.
Due to the rapid global growth in the usage of Buy Now, Pay Later service, it is crucial for e-commerce website to be able to accurately identify and predict potential users of this service and promote its use. By reducing payment transparency to increase impulse purchase, which can help increase customer transaction value and consumer satisfaction. Therefore, this study uses machine learning techniques to establish a predictive model for Buy Now, Pay Later consumers to provide e-commerce website with more effective promotion and precision marketing to increase service usage rates.
The study utilizes consumer transaction data from e-commerce website, including purchase frequency, amount, product preferences, and payment preferences. Given the class imbalance problem in the original dataset, the research employs six data imbalance techniques, such as Random Over Sampling, Random Under Sampling, SMOTE, ADASYN, ENN, and SMOTE+ENN, to improve the model prediction ability for minority samples. During the model building phase, six types of machine learning, including Logistic Regression, Random Forest, Gradient Boosting, KNN, Naive Bayes, and Neural Networks, along with 10-fold cross-validation methods to evaluate the results of each model. Comparing different data imbalance techniques and machine learning models, the model preprocessed with SMOTE+ENN and applied with Gradient Boosting performs best overall and in terms of stability.

關鍵字(中)

★ 機器學習
★ 先買後付
★ 資料不平衡
★ 監督式學習
★ 電子商務

關鍵字(英)

★ Machine Learning
★ Buy Now Pay Later
★ Data Imbalance
★ Supervised Learning
★ E-commerce Website

論文目次

致謝 I
摘要 II
ABSTRACT III
目錄 V
表目錄 VIII
圖目錄 IX
第一章緒論 1
1.1 研究背景 1
1.2 研究動機 5
1.3 研究目的 6
第二章文獻探討 8
2.1 資料探勘技術在電子商務中的應用 8
2.1.1 資料探勘技術應用於預測消費者行為偏好 8
2.1.2 資料探勘技術應用於消費者個性化推薦 9
2.1.3 資料探勘技術應用於信用卡偽冒預測 9
2.2 先買後付概述和發展趨勢 9
2.3 先買後付消費者行為研究 10
2.3.1 先買後付降低支付痛感以提升購物意願 10
2.3.2 心理因素影響使用先買後付 12
2.3.3 服務便利性影響使用先買後付 14
2.3.3 經濟和社會影響使用先買後付 15
2.3.3 先買後付服務對消費者的影響 15
2.4 本章小結 17
第三章研究方法 18
3.1 研究架構 18
3.2 資料來源 19
3.3 資料前處理 20
3.4 資料不平衡處理 23
3.4.1 過採樣-隨機過採樣 25
3.4.2 過採樣-SMOTE 25
3.4.3 過採樣-ADASYN 26
3.4.4 欠採樣-隨機欠採樣 27
3.4.5 欠採樣-ENN 27
3.4.3 綜合採樣-SMOTE+ENN 28
3.5 研究變數說明 29
3.6 機器學習技術 30
3.6.1 邏輯迴歸 30
3.6.2 隨機森林 31
3.6.3 梯度提升技術 32
3.6.4 K-近鄰演算法(KNN) 33
3.6.5 樸素貝葉斯 33
3.6.6 神經網路 34
3.7 實驗設計與評估 35
3.7.1 實驗設計 35
3.7.2 評估指標 38
3.7.2 實驗環境 42
第四章實驗結果分析 44
4.1 描述性統計分析 44
4.1.1 數值型變數分析 44
4.1.2 類別型變數分析 45
4.2 實驗一：原始資料集預測結果 47
4.3 實驗二：探討不同資料不平衡方法 47
4.4 綜合討論 53
第五章結論 55
5.1 研究結論與貢獻 55
5.2 研究限制 56
5.3 未來研究方向 57
參考文獻 59
附錄 65

參考文獻

中文部分：
王仁宏, & 李浩. (2020). 探討不同支付方式之消費痛感 [Explore the Pain of Paying in Different Payment Ways]. 行銷科學學報, 16(2), 105-135.
洪迎禎. (2021). 深度學習在電商平臺中用戶行為預測之運用國立臺灣大學].
許文華. (2019). 深度學習在電商平臺中產品推薦之運用國立臺北科技大學]. 臺灣博碩士論文知識加值系統. 台北市.
陳佩妤. (2018). 利用深度學習預測信用卡詐騙世新大學]. 臺灣博碩士論文知識加值系統. 臺北市.
陳昱安. (2023). 影響消費者採用先買後付服務模式之研究世新大學]. 臺灣博碩士論文知識加值系統. 臺北市.
蔡秉修. (2014). 應用資料探勘於電子商務之消費行為研究-以音樂購物網為例 (Publication Number 2014年) 國立臺北科技大學]. AiritiLibrary.
蔡柏堅. (2021). 信用卡詐欺偵測:結合三域安全協議及機器學習的應用架構國立臺北科技大學]. 臺灣博碩士論文知識加值系統. 台北市.
鄭菀瑜. (2024). 探討先買後付的支付模式對消費者之衝動消費行為的影響國立中山大學]. 臺灣博碩士論文知識加值系統. 高雄市.

英文部分：
Aisjah, S. (2024). Intention to use buy-now-pay-later payment system among university students: a combination of financial parenting, financial self-efficacy, and social media intensity. Cogent Social Sciences.
Alvarez, A. S. G. (2021). Buy-now Pay-later: Business Models and Market Overview Politecnico di Milano].
Bangia, M., Harrison, L., Plotkin, C. L., & Piwonski, K. (2022). Busting the five biggest B2B e-commerce myths. McKinsey & Company.
Batista, G., Bazzan, A., & Monard, M.-C. (2003). Balancing Training Data for Automated Annotation of Keywords: a Case Study.
Bilal, M., Siti Nuraqilah Binti Ahmad, T., Muhammad Kashif, S., Fong Woon, L., & Nasir Abdul, J. (2023). A Proposed Framework for Assessing BNPL (Buy Now, Pay Later) Adoption and its Impact on Consumers′ Buying Behavior. KnE Social Sciences.
Boxell, J. (2021). How old-style ′Buy Now, Pay Later′ became trendy ‘BNPL’. Bloomberg.
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16, 321-357.
Desikan, V. (2023). A Study on Customer Preferences towards Buy Now Pay Later (BNPL) Services and its Impact on their Financial Well-Being in Chennai City. Journal of Development Economics and Management Research Studies.
Fisher, C., Holland, C., & West, T. (2021). Developments in the buy now, pay later market [Aufsatz in Zeitschrift, Article in journal]. Bulletin / Reserve Bank of Australia((3)), 59-71.
Guttman-Kenney, B., Firth, C., & Gathergood, J. (2023). Buy now, pay later (BNPL) ...on your credit card. Journal of Behavioral and Experimental Finance, 37, 100788.
Haibo, H., Yang, B., Garcia, E. A., & Shutao, L. (2008, 1-8 June 2008). ADASYN: Adaptive synthetic sampling approach for imbalanced learning. 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence),
Harfiahani, I., Rakhma, N., Prodi, A., Fakultan, E., Dan, B., & Pasuruan, I. Y. (2022). PENGARUH FINANCIAL ATTITUDE DAN SELF CONTROL TERHADAP FINANCIAL MANAGEMENT BEHAVIOR DALAM MEMANFAATKAN PELAYANAN BUY NOW PAY LATER. Jurnal Transparan STIE Yadika Bangil.
InsiderIntelligence. (2024). The Growth in Retail E-commerce Sales Worldwide 2022-2027 (in trillions). Insider Intelligence.
Islam, A. (2023). «Decoding Buy Now, Pay Later in Egypt: A Dive into Adoption Drivers and Financial Behaviors».
Kemper, J., & Deufel, P. (2018). How the Purchase Situation Affects the Payment Method Choice in E-Commerce. Academy of Management Proceedings,
Marco, D. M., Justin, K., & Emily, W. (2022). Buy Now, Pay Later Credit: User Characteristics and Effects on Spending Patterns.
Maurya, A., Pratap, S., Pratap, P., & Dwivedi, A. (2023, 28-30 April 2023). Analysis of Behavioural Data of Customer for the E-Commerce Platform by using Machine Learning Approach. 2023 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES),
Muhammad Arif, H., & Muhammad, H. (2022). The Analysis of the “Buy Now, Pay Later” Use Intention in Indonesia. International Journal of Current Science Research and Review.
Papikyan, A. (2023). Buy Now, Pay Later: A critical review of the system and its implementation based on an analysis on currently working countries and based on a consumer survey [Master [120] : ingénieur de gestion, à finalité spécialisée, Université catholique de Louvain (UCL)]. Handle:
Prelec, D., & Loewenstein, G. (1998). The Red and the Black: Mental accounting of savings and debt. Marketing Science, 17(1), 4-28.
Qin, X. (2022). Research on loyalty prediction of e-commerce customer based on data mining. Applied Mathematics and Nonlinear Sciences, 8.
Qurniawati, R. S., Nurohman, Y. A., & Andreyan, D. R. (2023). Predicting Compulsive Buying Because of Buy Now Pay Later Installment. Al Tijarah, 9(1), 14 - 26.
Rafidarma K, M., & Aprilianty, F. (2022). The Impact Buy Now Pay Later Feature Towards Online Buying Decision in E-Commerce Indonesia. International Journal of Business and Technology Management(3), 155-162%V 154.
Relja, R., Ward, P., & Zhao, A. (2023). Understanding the psychological determinants of buy-now-pay-later (BNPL) in the UK (Vol. 42).
Robert, P., Anh, D., Denise, G., & Jaime, Y. (2023). The relationship between responsible financial behaviours and financial wellbeing: The case of buy‐now‐pay‐later. Accounting & Finance.
Shevlin, R. (2021). Buy Now, Pay Later: The “New” Payments Trend Generating $100 Billion In Sales.
Swetha, S. (2022). A Research on Buy Now Pay Later Model of Amazon. International journal of engineering technology and management sciences,.
Thakur, A. (2021). Trends and analysis of e-commerce market: a global perspective. INTERNATIONAL JOURNAL OF APPLIED MARKETING, 6, 11-22.

指導教授

胡雅涵周恩頤(Ya-Han Hu En-Yi Chou)

審核日期

2024-6-26

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