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研究生: 張皓偉
Chang, Hao-Wei
論文名稱: 以羅吉斯迴歸分析行動支付平台之B2B客戶流失
Using Logistic Regression to Analyze B2B Customer Churn in Mobile Payment Platform
指導教授: 呂執中
Lyu, Jr-Jung
學位類別: 碩士
Master
系所名稱: 管理學院 - 資訊管理研究所
Institute of Information Management
論文出版年: 2021
畢業學年度: 109
語文別: 中文
論文頁數: 71
中文關鍵詞: 行動支付B2B客戶流失羅吉斯迴歸Push-Pull-Mooring Model
外文關鍵詞: Mobile Payment, B2B, Customer Churn, Logistic Regression, Push-Pull-Mooring Model
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  • 當前的行動支付產業競爭激烈,市場上各家行動支付平台皆試圖搶佔市場,而民眾對行動支付的接受度也越來越高,在消費者常用的交易方式中,行動支付從 2018 年 43.8%,成長至 2020 上半年的 59.7%,當各家行動支付平台在搶得市佔後,如何守住現有戰果便是接下來的課題,目前企業普遍意識到開發一個新客戶的成本要比留住既有客戶高得多,因此需要理解客戶流失背後的關鍵因素,並將客戶分類,在流失發生前留住有價值的客戶。行動支付平台因為屬於雙邊市場,商家的地位與消費者相當,且目前平台的直接收益有一大部分來自商家,因此如何留住B2B客戶便是行動支付平台所面對的一大課題。
    本研究使用Push-Pull-Mooring 模型,分析影響行動支付平台B2B客戶流失背後的推力(Push)、拉力(Pull)、繫住力(Mooring),結合過去研究分析客戶流失時常用的RFM模型。研究透過問卷發放來獲取商家的資料,並使用羅吉斯迴歸驗證理論模型,找出流失商家和非流失商家的特徵,探討影響行動支付平台商家流失的關鍵因素。
    研究結果發現資安風險、滿意度、替代方案的吸引力、交易頻率、交易額是分析行動支付平台商家流失的關鍵因素,其中交易頻率是最重要的變數,在管理意涵中,建議平台業者可以針對平台資訊安全的部分優先改善,同時維持其服務品質、系統品質、資訊品質,讓用戶感受到其服務穩定、資訊可靠,針對用戶轉移到不同平台所造成的客戶流失,平台應提供更優惠的合作方案、較佳的功能性、較多的用戶,為行動支付平台在保留客戶上帶來競爭優勢,新進平台則可以透過這些吸引力來誘使用戶轉移,而收集客戶的交易頻率、交易量來建立客戶行為資料庫,可以在未來針對客戶流失執行預測性分析。

    Currently, the mobile payment industry in Taiwan is highly competitive. Therefore, after each mobile payment platform has gained its market share, determining how to retain customers has become a key issue. Currently, companies generally realize that the cost of developing a new customer is much higher than retaining existing customers. Companies need to understand the key factors behind customer churn and how to classify customers in order to retain valuable customers before churn occurs. A mobile payment platform is a two-sided platform; hence, B2B customers have the same value as B2C customers. However, there is currently few research on B2B customer churn on mobile payment platforms. An empirical study to investigate the key factors behind B2B customer churn on mobile payment platforms, using the Push-Pull-Mooring model and RFM (recency, frequency, monetary value) model, was developed. A logistic regression was used to verify the research model. The results of the analysis indicated that security risks, satisfaction, alternative attractiveness, frequency, monetary value are the key factors. Among all the factors, frequency was the most important factor. Our findings can be used by mobile payment platforms to identify priority improvement targets when improving their systems.

    摘要 i Extended Abstract ii 致謝 xi 目錄 xii 圖目錄 xv 表目錄 xvi 第一章 緒論 1 1.1 研究背景 1 1.2 研究動機 3 1.3 研究目的 5 1.4 研究限制 5 1.5 研究流程 6 第二章 文獻回顧 7 2.1 行動支付 7 2.1.1 行動支付的定義及特性 7 2.1.2 台灣行動支付平台的定義與分類 10 2.1.3 行動支付的研究概況 11 2.2 PPM模型 12 2.3 客戶流失預測 14 第三章 研究方法 18 3.1 研究架構 18 3.2 研究假說 19 3.2.1 資安風險 19 3.2.2 隱私風險 20 3.2.3 滿意度 20 3.2.4 轉換成本 21 3.2.5 替代方案的吸引力 22 3.2.6 沉沒成本 22 3.2.7 感知有用性和易用性 23 3.2.8 客戶行為 23 3.3 問卷設計 24 3.3.1 推力構面 24 3.3.2 拉力構面 25 3.3.3 繫住力構面 26 3.3.4 客戶行為構面 27 3.3.5 客戶流失 27 3.4 抽樣設計 28 3.5 問卷前測 28 3.6 資料分析方法 30 3.6.1 敘述統計分析 30 3.6.2 羅吉斯迴歸 30 3.6.3 分類模型評估指標 33 第四章 研究結果與分析 35 4.1 敘述性統計分析 35 4.1.1 樣本基本資料 35 4.1.2 變項之敘述統計 38 4.2 羅吉斯迴歸分析 42 4.2.1 資料處理 43 4.2.2 共線性診斷 44 4.2.3 模型配適度 46 4.2.4 迴歸係數解釋 47 4.2.5 模型分類性能 51 4.3 小結 54 第五章 結論與建議 57 5.1 研究結論 57 5.2 管理意涵 58 5.3 未來研究方向與建議 59 參考文獻 60 附錄一 正式問卷 68

    Abbas, A. E. (2006). Entropy methods for joint distributions in decision analysis. Ieee Transactions on Engineering Management, 53(1), 146-159.
    Ahn, J.-H., Han, S.-P., & Lee, Y.-S. (2006). Customer churn analysis: Churn determinants and mediation effects of partial defection in the Korean mobile telecommunications service industry. Telecommunications Policy, 30(10-11), 552-568.
    Ahn, J., Hwang, J., Kim, D., Choi, H., & Kang, S. (2020). A Survey on Churn Analysis in Various Business Domains. IEEE Access, 8, 220816-220839.
    Ajina, A., & Habib, A. (2017). Examining the relationship between Earning management and market liquidity. Research in International Business and Finance, 42, 1164-1172.
    Al-Mashraie, M., Chung, S. H., & Jeon, H. W. (2020). Customer switching behavior analysis in the telecommunication industry via push-pull-mooring framework: A machine learning approach. Computers & Industrial Engineering, 144, 106476.
    Alkhowaiter, W. A. (2020). Digital payment and banking adoption research in Gulf countries: A systematic literature review. International Journal of Information Management, 53, 102102.
    Amin, A., Shah, B., Khattak, A. M., Moreira, F. J. L., Ali, G., Rocha, A., & Anwar, S. (2019). Cross-company customer churn prediction in telecommunication: A comparison of data transformation methods. International Journal of Information Management, 46, 304-319.
    Ascarza, E., Netzer, O., & Hardie, B. G. S. (2018). Some Customers Would Rather Leave Without Saying Goodbye. Marketing Science, 37(1), 54-77.
    Bansal, H. S., Taylor, S. F., & St. James, Y. (2005). “Migrating” to new service providers: Toward a unifying framework of consumers’ switching behaviors. Journal of the Academy of Marketing Science, 33(1), 96-115.
    Barfar, A., Padmanabhan, B., & Hevner, A. (2017). Applying behavioral economics in predictive analytics for B2B churn: Findings from service quality data. Decision Support Systems, 101, 115-127.
    Berger, P., & Kompan, M. (2019). User Modeling for Churn Prediction in E-Commerce. Ieee Intelligent Systems, 34(2), 44-52.
    Burnham, T. A., Frels, J. K., & Mahajan, V. (2003). Consumer switching costs: a typology, antecedents, and consequences. Journal of the Academy of Marketing Science, 31(2), 109-126.
    Cao, X., Yu, L., Liu, Z., Gong, M., & Adeel, L. (2018). Understanding mobile payment users’ continuance intention: a trust transfer perspective. Internet Research.
    Chandra, S., Srivastava, S. C., & Theng, Y.-L. (2010). Evaluating the role of trust in consumer adoption of mobile payment systems: An empirical analysis. Communications of the association for information systems, 27(1), 29.
    Chang, I. C., Liu, C. C., & Chen, K. (2014). The push, pull and mooring effects in virtual migration for social networking sites. Information Systems Journal, 24(4), 323-346.
    Chen, K., Hu, Y.-H., & Hsieh, Y.-C. (2015). Predicting customer churn from valuable B2B customers in the logistics industry: a case study. Information Systems and e-Business Management, 13(3), 475-494.
    Cheng, S., Lee, S.-J., & Choi, B. (2019). An empirical investigation of users’ voluntary switching intention for mobile personal cloud storage services based on the push-pull-mooring framework. Computers in Human Behavior, 92, 198-215.
    Cheung, C. M., Chan, G. W., & Limayem, M. (2005). A critical review of online consumer behavior: Empirical research. Journal of electronic commerce in organizations (JECO), 3(4), 1-19.
    Coussement, K., & De Bock, K. W. (2013). Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning. Journal of Business Research, 66(9), 1629-1636.
    Coussement, K., Lessmann, S., & Verstraeten, G. (2017). A comparative analysis of data preparation algorithms for customer churn prediction: A case study in the telecommunication industry. Decision Support Systems, 95, 27-36.
    Coussement, K., Phan, M., De Caigny, A., Benoit, D. F., & Raes, A. (2020). Predicting student dropout in subscription-based online learning environments: The beneficial impact of the logit leaf model. Decision Support Systems, 135, 113325.
    Cox, D. R., & Snell, E. J. (2018). Analysis of binary data: Routledge.
    Cronbach, L. J. (1957). The two disciplines of scientific psychology. American psychologist, 12(11), 671.
    Dahlberg, T., Guo, J., & Ondrus, J. (2015). A critical review of mobile payment research. Electronic Commerce Research and Applications, 14(5), 265-284.
    Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
    De Caigny, A., Coussement, K., & De Bock, K. W. (2018). A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees. European Journal of Operational Research, 269(2), 760-772.
    de Luna, I. R., Liébana-Cabanillas, F., Sánchez-Fernández, J., & Muñoz-Leiva, F. (2019). Mobile payment is not all the same: The adoption of mobile payment systems depending on the technology applied. Technological Forecasting and Social Change, 146, 931-944.
    de Reuver, M., Verschuur, E., Nikayin, F., Cerpa, N., & Bouwman, H. (2015). Collective action for mobile payment platforms: A case study on collaboration issues between banks and telecom operators. Electronic Commerce Research and Applications, 14(5), 331-344.
    Fang, Y.-H., & Tang, K. (2017). Involuntary migration in cyberspaces: The case of MSN messenger discontinuation. Telematics and informatics, 34(1), 177-193.
    Figalist, I., Elsner, C., Bosch, J., & Olsson, H. H. (2019). Customer Churn Prediction in B2B Contexts. Paper presented at the International Conference on Software Business.
    Gong, X., Zhang, K. Z. K., Chen, C. Y., Cheung, C. M. K., & Lee, M. K. O. (2020). What drives self-disclosure in mobile payment applications? The effect of privacy assurance approaches, network externality, and technology complementarity. Information Technology & People, 33(4), 1174-1213.
    Gordini, N., & Veglio, V. (2017). Customers churn prediction and marketing retention strategies. An application of support vector machines based on the AUC parameter-selection technique in B2B e-commerce industry. Industrial Marketing Management, 62, 100-107.
    Guo, J., & Bouwman, H. (2016). An analytical framework for an m-payment ecosystem: A merchants׳ perspective. Telecommunications Policy, 40(2), 147-167.
    Gupta, A., Yousaf, A., & Mishra, A. (2020). How pre-adoption expectancies shape post-adoption continuance intentions: An extended expectation-confirmation model. International Journal of Information Management, 52, 13.
    Gupta, S., & Zeithaml, V. (2006). Customer metrics and their impact on financial performance. Marketing Science, 25(6), 718-739.
    Gustafsson, A., Johnson, M. D., & Roos, I. (2005). The effects of customer satisfaction, relationship commitment dimensions, and triggers on customer retention. Journal of Marketing, 69(4), 210-218.
    Höppner, S., Stripling, E., Baesens, B., Broucke, S. v., & Verdonck, T. (2020). Profit driven decision trees for churn prediction. European Journal of Operational Research, 284(3), 920-933.
    Hammoudeh, A., Fraihat, M., & Almomani, M. (2019, 9-11 April 2019). Selective Ensemble Model for Telecom Churn Prediction. Paper presented at the 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT).
    Hanif, A., & Azhar, N. (2017). Resolving class imbalance and feature selection in customer churn dataset. Paper presented at the 2017 International Conference on Frontiers of Information Technology (FIT).
    Hill, C. W., Jones, G. R., & Schilling, M. A. (2014). Strategic management: theory: an integrated approach: Cengage Learning.
    Hoffman, D. L., Novak, T. P., & Peralta, M. (1999). Building consumer trust online. Communications of the ACM, 42(4), 80-85.
    Holzer, A., & Ondrus, J. (2011). Mobile application market: A developer’s perspective. Telematics and informatics, 28(1), 22-31.
    Hsieh, J.-K., Hsieh, Y.-C., Chiu, H.-C., & Feng, Y.-C. (2012). Post-adoption switching behavior for online service substitutes: A perspective of the push–pull–mooring framework. Computers in Human Behavior, 28(5), 1912-1920.
    Idris, A., Iftikhar, A., & Rehman, Z. U. (2019). Intelligent churn prediction for telecom using GP-AdaBoost learning and PSO undersampling. Cluster Computing-the Journal of Networks Software Tools and Applications, 22, S7241-S7255.
    Johnson, V. L., Kiser, A., Washington, R., & Torres, R. (2018). Limitations to the rapid adoption of M-payment services: Understanding the impact of privacy risk on M-Payment services. Computers in Human Behavior, 79, 111-122.
    Jones, M. A., Mothersbaugh, D. L., & Beatty, S. E. (2002). Why customers stay: measuring the underlying dimensions of services switching costs and managing their differential strategic outcomes. Journal of Business Research, 55(6), 441-450.
    Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31-36.
    Kandeil, D. A., Saad, A. A., & Youssef, S. M. (2014). A two-phase clustering analysis for B2B customer segmentation. Paper presented at the 2014 International Conference on Intelligent Networking and Collaborative Systems.
    Kaya, E., Dong, X., Suhara, Y., Balcisoy, S., & Bozkaya, B. (2018). Behavioral attributes and financial churn prediction. EPJ Data Science, 7(1), 41.
    Keaveney, S. M., & Parthasarathy, M. (2001). Customer switching behavior in online services: An exploratory study of the role of selected attitudinal, behavioral, and demographic factors. Journal of the Academy of Marketing Science, 29(4), 374-390.
    Khalilzadeh, J., Ozturk, A. B., & Bilgihan, A. (2017). Security-related factors in extended UTAUT model for NFC based mobile payment in the restaurant industry. Computers in Human Behavior, 70, 460-474.
    Kim, H.-W., & Kankanhalli, A. (2009). Investigating user resistance to information systems implementation: A status quo bias perspective. MIS quarterly, 567-582.
    Kumar, A., Adlakaha, A., & Mukherjee, K. (2018). The effect of perceived security and grievance redressal on continuance intention to use M-wallets in a developing country. International Journal of Bank Marketing.
    Kumar, M., & Rath, S. K. (2016). Chapter 15 - Feature Selection and Classification of Microarray Data Using Machine Learning Techniques. In Q. N. Tran & H. R. Arabnia (Eds.), Emerging Trends in Applications and Infrastructures for Computational Biology, Bioinformatics, and Systems Biology (pp. 213-242). Boston: Morgan Kaufmann.
    Kuo, R.-Z. (2020). Why do people switch mobile payment service platforms? An empirical study in Taiwan. Technology in Society, 62, 101312.
    Lee, E., Kim, B., Kang, S., Kang, B., Jang, Y., & Kim, H. K. (2018). Profit optimizing churn prediction for long-term loyal customers in online games. IEEE Transactions on Games, 12(1), 41-53.
    Leinonen, H. (2009). The changing retail payments landscape: An overview. THE CHANGING RETAIL PAYMENTS LANDSCAPE, 11.
    Linck, K., Pousttchi, K., & Wiedemann, D. G. (2006). Security issues in mobile payment from the customer viewpoint.
    Lowry, P. B., Cao, J., & Everard, A. (2011). Privacy concerns versus desire for interpersonal awareness in driving the use of self-disclosure technologies: The case of instant messaging in two cultures. Journal of Management Information Systems, 27(4), 163-200.
    Lu, J., Wei, J., Yu, C.-S., & Liu, C. (2017). How do post-usage factors and espoused cultural values impact mobile payment continuation? Behaviour & Information Technology, 36(2), 140-164.
    Mallat, N. (2007). Exploring consumer adoption of mobile payments–A qualitative study. The Journal of Strategic Information Systems, 16(4), 413-432.
    Martens, D., Vanthienen, J., Verbeke, W., & Baesens, B. (2011). Performance of classification models from a user perspective. Decision Support Systems, 51(4), 782-793.
    Metzger, M. J. (2004). Privacy, trust, and disclosure: Exploring barriers to electronic commerce. Journal of computer-mediated communication, 9(4), JCMC942.
    Moeyersoms, J., & Martens, D. (2015). Including high-cardinality attributes in predictive models: A case study in churn prediction in the energy sector. Decision Support Systems, 72, 72-81.
    Moon, B. (1995). Paradigms in migration research: exploring'moorings' as a schema. Progress in human geography, 19(4), 504-524.
    Nagelkerke, N. J. (1991). A note on a general definition of the coefficient of determination. Biometrika, 78(3), 691-692.
    Neter, J., Kutner, M. H., Nachtsheim, C. J., & Wasserman, W. (1996). Applied linear statistical models.
    Nie, G., Rowe, W., Zhang, L., Tian, Y., & Shi, Y. (2011). Credit card churn forecasting by logistic regression and decision tree. Expert Systems with Applications, 38(12), 15273-15285.
    Njite, D., Kim, W. G., & Kim, L. H. (2008). Theorizing consumer switching behavior: A general systems theory approach. Journal of Quality Assurance in Hospitality & Tourism, 9(3), 185-218.
    Patil, P., Tamilmani, K., Rana, N. P., & Raghavan, V. (2020). Understanding consumer adoption of mobile payment in India: Extending Meta-UTAUT model with personal innovativeness, anxiety, trust, and grievance redressal. International Journal of Information Management, 54, 102144.
    Pousttchi, K. (2003). Conditions for acceptance and usage of mobile payment procedures.
    Samuelson, W., & Zeckhauser, R. (1988). Status quo bias in decision making. Journal of risk and uncertainty, 1(1), 7-59.
    Sebola, L., & Penzhorn, W. (2003). A secure M-Commerce system for the vending of prepaid electricity tokens. Paper presented at the Southern African Telecommunication Networks and Applications Conference (SATNAC).
    Shin. (2010). The effects of trust, security and privacy in social networking: A security-based approach to understand the pattern of adoption. Interacting with computers, 22(5), 428-438.
    Shin, Y. M., Lee, S. C., Shin, B., & Lee, H. G. (2010). Examining influencing factors of post-adoption usage of mobile internet: Focus on the user perception of supplier-side attributes. Information Systems Frontiers, 12(5), 595-606.
    Shirazi, F., & Mohammadi, M. (2019). A big data analytics model for customer churn prediction in the retiree segment. International Journal of Information Management, 48, 238-253.
    Sinnott, R. O., Duan, H., & Sun, Y. (2016). Chapter 15 - A Case Study in Big Data Analytics: Exploring Twitter Sentiment Analysis and the Weather. In R. Buyya, R. N. Calheiros, & A. V. Dastjerdi (Eds.), Big Data (pp. 357-388): Morgan Kaufmann.
    Soares-Aguiar, A., & Palma-Dos-Reis, A. (2008). Why do firms adopt e-procurement systems? Using logistic regression to empirically test a conceptual model. Ieee Transactions on Engineering Management, 55(1), 120-133.
    Tamaddoni Jahromi, A., Stakhovych, S., & Ewing, M. (2014). Managing B2B customer churn, retention and profitability. Industrial Marketing Management, 43(7), 1258-1268.
    Tang, Z., & Chen, L. (2020). An empirical study of brand microblog users’ unfollowing motivations: The perspective of push-pull-mooring model. International Journal of Information Management, 52, 102066.
    Uner, M. M., Guven, F., & Cavusgil, S. T. (2020). Churn and loyalty behavior of Turkish digital natives: Empirical insights and managerial implications. Telecommunications Policy, 44(4), 101901.
    Velleman, P. F., & Welsch, R. E. (1981). Efficient computing of regression diagnostics. The American Statistician, 35(4), 234-242.
    Verbeke, W., Dejaeger, K., Martens, D., Hur, J., & Baesens, B. (2012). New insights into churn prediction in the telecommunication sector: A profit driven data mining approach. European Journal of Operational Research, 218(1), 211-229.
    Verbeke, W., Martens, D., Mues, C., & Baesens, B. (2011). Building comprehensible customer churn prediction models with advanced rule induction techniques. Expert Systems with Applications, 38(3), 2354-2364.
    Verma, S., Chaurasia, S. S., & Bhattacharyya, S. S. (2019). The effect of government regulations on continuance intention of in-store proximity mobile payment services. International Journal of Bank Marketing.
    Wang, L., Luo, X., Yang, X., & Qiao, Z. (2019). Easy come or easy go? Empirical evidence on switching behaviors in mobile payment applications. Information & Management, 56(7), 103150.
    Wu, X., & Meng, S. (2016). E-commerce customer churn prediction based on improved SMOTE and AdaBoost. Paper presented at the 2016 13th International Conference on Service Systems and Service Management (ICSSSM).
    Xu, Y. C., Yang, Y., Cheng, Z., & Lim, J. (2014). Retaining and attracting users in social networking services: An empirical investigation of cyber migration. The Journal of Strategic Information Systems, 23(3), 239-253.
    Yang, H.-L., & Lin, S.-L. (2015). User continuance intention to use cloud storage service. Computers in Human Behavior, 52, 219-232.
    Ye, C., & Potter, R. (2011). The role of habit in post-adoption switching of personal information technologies: An empirical investigation. Communications of the association for information systems, 28(1), 35.
    Yu, X., Guo, S., Guo, J., & Huang, X. (2011). An extended support vector machine forecasting framework for customer churn in e-commerce. Expert Systems with Applications, 38(3), 1425-1430.
    Zhang, K. Z., Cheung, C. M., & Lee, M. K. (2012). Online service switching behavior: The case of blog service providers. Journal of Electronic Commerce Research, 13(3), 184.
    Zhang, L., Zhu, J., & Liu, Q. (2012). A meta-analysis of mobile commerce adoption and the moderating effect of culture. Computers in Human Behavior, 28(5), 1902-1911.
    Zhou, T. (2013). An empirical examination of continuance intention of mobile payment services. Decision Support Systems, 54(2), 1085-1091.

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