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    政大典藏 > College of Commerce > Department of MIS > Theses >  Item 140.119/136348


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/136348


    Title: 處方異質性下正向表達對P2P借貸績效的影響
    Effect of positive expression on P2P lending performance under treatment heterogeneity
    Authors: 劉晉豪
    Liu, Chin-Hau
    Contributors: 莊皓鈞
    周彥君
    Chuang, Hao-Chun
    Chou, Yen-Chun
    劉晉豪
    Liu, Chin-Hau
    Keywords: P2P借貸
    因果森林
    機器學習
    處方效應
    異質性
    P2P lending
    Causal forest
    machine learning
    treatment effect
    heterogeneity
    Date: 2021
    Issue Date: 2021-08-04 14:48:32 (UTC+8)
    Abstract: 有鑒於網路的便捷加上群眾募資及網路借貸平台的日趨普及，越來越多使 用者利用這類平台進行融資借貸，因此如何在眾多平台的使用者中脫穎而出成 為每個借方最迫切了解的方向，要選擇放上有笑容的圖片帶給閱聽者充滿信 心、勤奮且有希望的募資者印象?還是不應該顯得如此樂觀，應該要盡量用各種 方式透露出自己的需求來博取同情?這兩個策略看似都屬合理，但卻是兩個完 全相反的策略。本研究探討的是圖片笑容與募款績效的提升是否有幫助，並以 每日可以募得到的資金以及平均每位貸方願意提供多少資金做為募資績效的評 比標準，此研究牽涉到因果推論及反事實的研究，相較於預測問題，本研究更 在乎的是因果關係的推論，因此採用建置因果森林的分析方法。除此之外，本 研究也另外分析異質性處方效應的發生時機，換言之，如果笑容確實可以改變 募款績效，那麼什麼樣的條件下可以將處方效應的影響發揮到最大的效果，藉 此研究幫助 P2P 借貸平台上的使用者可以針對自己的募款情況擬定相關的募資 策略，希望對消弭世界的資訊不對稱以及資金的流通有所貢獻。
    Given the convenience of the Internet and the growing popularity of crowdfunding and P2P lending platforms, more and more users are using these platforms to raise money and borrow money, so how to stand out from the crowd of platform users has become the most pressing issue that every borrower wants to understand. Should I choose a picture with a smile on it to convey to the reader that I am a confident, hardworking and hopeful fundraiser? Or should I not appear so optimistic and try to gain sympathy by revealing my needs in every way possible? Both of these strategies may seem reasonable, but they are two completely opposite strategies. This study examines whether smiles are associated with improved fundraising performance, and evaluates fundraising performance in terms of the amount of money raised per day and the average amount of money each lender is willing to provide. This study involves causal inference and counterfactual research. We are more concerned with causal inference than prediction problems, so this study uses a causal forest analysis method. In addition, this study also analyzes the timing of heterogeneity treatment effects, in other words, if smiles are found to change fundraising performance, then what conditions can maximize the impact of treatment effects. This research helps users of P2P lending platforms to develop fundraising strategies for their own fundraising situations, in the hope of contributing to the elimination of information asymmetry and the flow of funds in the world.
    Reference: Abadie, A., & Imbens, G. W. (2006). Large sample properties of matching estimators for average treatment effects. Econometrica (Vol. 74, Issue 1, pp. 235–267).
    Allison, T. H., Davis, B. C., Short, J. C., & Webb, J. W. (2015). Crowdfunding in a prosocial microlending environment: Examining the role of intrinsic versus extrinsic cues. Entrepreneurship: Theory and Practice, 39(1), 53–73.
    Allison, T. H., McKenny, A. F., & Short, J. C. (2013). The effect of entrepreneurial rhetoric on microlending investment: An examination of the warm-glow effect. Journal of Business Venturing, 28(6), 690–707.
    Anglin, A. H., Short, J. C., Drover, W., Stevenson, R. M., McKenny, A. F., & Allison, T. H. (2018). The power of positivity? The influence of positive psychological capital language on crowdfunding performance. Journal of Business Venturing, 33(4), 470–492.
    Angrist, J. D. (2004). Treatment effect heterogeneity in theory and practice. Economic Journal, 114(494), 52–83.
    Athey, S., & Imbens, G. (2016). Recursive partitioning for heterogeneous causal effects. In Proceedings of the National Academy of Sciences of the United States of America, 113(27), 7353-7360.
    Athey, S., Tibshirani, J., & Wager, S. (2019). Generalized random forests. Annals of Statistics, 47(2), 1179–1203.
    Athey, S., & Wager, S. (2017). Efficient Policy Learning. Working Paper.
    Avey, J. B., Reichard, R. J., Luthans, F., & Mhatre, K. H. (2009). Meta-Analysis of the Impact of Positive Psychological Capital on Employee Attitudes, Behaviors,
    and Performance. Human Resouce Development Quarterly, 22(2), 128–152.
    Bachmann, A., Becker, A., Buerckner, D., Hilker, M., Kock, F., Lehmann, M., & Tiburtius, P. (2011). Online Peer-to-Peer Lending – A Literature Review. Journal of Internet Banking and Commerce, 16(2), 1–18.
    Baiardi, A., & Naghi, A. (2021). The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies. Working Paper.
    Bollinger, B., & Yao, S. (2018). Risk transfer versus cost reduction on two-sided microfinance platforms. Quantitative Marketing and Economics, 16(3).
    Burtch, G., Ghose, A., & Wattal, S. (2014). Cultural Differences and Geography as Determinants of Online Prosocial Lending. MIS Quarterly, 38(3), 773–794.
    Charles F. Manski. (2008). Studying Treatment Response to Inform Treatment Choice. Annales d’Économie et de Statistique, 93–105.
    Davila, A., Foster, G., & Gupta, M. (2003). Venture capital financing and the growth of startup firms. Journal of Business Venturing, 18(6), 689–708.
    Davis, B. C., Hmieleski, K. M., Webb, J. W., & Coombs, J. E. (2017). Funders’ positive affective reactions to entrepreneurs’ crowdfunding pitches: The influence of perceived product creativity and entrepreneurial passion. Journal of Business Venturing, 32(1), 90–106.
    Davis, J. M. V., & Heller, S. B. (2017). Using causal forests to predict treatment heterogeneity: An application to summer jobs. American Economic Review, 107(5), 546–550.
    Dehejia, R. H., & Wahba, S. (2002). Propensity score-matching methods for nonexperimental causal studies. Review of Economics and Statistics, 84(1), 151– 161.
    Galak, J., Small, D., & Stephen, A. T. (2011). Microfinance decision making: A field study of prosocial lending. Journal of Marketing Research, 48(SPEC. ISSUE), 130–137.
    Haas, P., Blohm, I., & Leimeister, J. M. (2014). An empirical taxonomy of crowdfunding intermediaries. 35th International Conference on Information Systems, 1–19.
    He, X., Zhang, X., & Xin, Q. (2018). Recognition of building group patterns in topographic maps based on graph partitioning and random forest. ISPRS Journal of Photogrammetry and Remote Sensing, 136, 26–40.
    Herzensten, M., Andrews, R. L., Dholakia, U. M., & Lyandres, E. (2008). The democratization of personal consumer loans? Determinants of success in online peer-to-peer lending communities. Working Paper.
    Jenq, C., Pan, J., & Theseira, W. (2011). What Do Donors Discriminate On? Evidence From Kiva.org.
    Kenton Anderson, & Saxton, G. D. (2016). Smiles, Babies, and Status Symbols The Persuasive Effects of Image Choices in Small-Entrepreneur Crowdfunding Requests. International Journal of Communication, 10, 1764–1785.
    Kirsch, D., Goldfarb, B., & Gera, A. (2009). Form or substance: the role of business plans in venture capital decision making. Strategic Management Journal, 30(5), 487–515.
    Klafft, M. (2008). Online peer-to-peer lending: A lenders’ perspective. Proceedings of the 2008 International Conference on E-Learning, e-Business, Enterprise Information Systems, and e-Government, EEE 2008, July, 371–375.
    Lua, M., Sadiqb, S., Feastera, D. J., & Ishwarana, H. (2018). Estimating Individual Treatment Effect in Observational Data Using Random Forest Methods. Journal of Computational and Graphical Statistics, 27(1), 209–219.
    Luthans, F., Luthans, K. W., & Luthans, B. C. (2004). Positive psychological capital: Beyond human and social capital. Business Horizons, 47(1), 45–50.
    Ly, P., & Mason, G. (2012a). Competition Between Microfinance NGOs: Evidence from Kiva. World Development, 40(3), 643–655.
    Ly, P., & Mason, G. (2012b). Individual Preferences Over Development Projects: Evidence from Microlending on Kiva. International Society for Third-Sector Research, 23(4), 1036–1055.
    McKenny, A. F., Allison, T. H., Ketchen, D. J., Short, J. C., & Ireland, R. D. (2017). How Should Crowdfunding Research Evolve? A Survey of the Entrepreneurship Theory and Practice Editorial Board. Entrepreneurship: Theory and Practice, 41(2), 291–304.
    Mollick, E. (2014). The dynamics of crowdfunding: An exploratory study. Journal of Business Venturing, 29(1), 1–16.
    Nie, X., & Wager, S. (2021). Quasi-oracle estimation of heterogeneous treatment effects. Biometrika, 108(2), 299–319.
    Parhankangas, A., & Ehrlich, M. (2014). How entrepreneurs seduce business angels: An impression management approach. Journal of Business Venturing, 29(4), 543–564.
    Parhankangas, A., & Renko, M. (2017). Linguistic style and crowdfunding success among social and commercial entrepreneurs. Journal of Business Venturing, 32(2), 215–236.
    Pollock, T. G., Chen, G., Jackson, E. M., & Hambrick, D. C. (2010). How much prestige is enough? Assessing the value of multiple types of high-status affiliates for young firms. Journal of Business Venturing, 25(1), 6–23.
    Robinson, P. M. (1988). Root-N-Consistent Semiparametric Regression. Econometrica, 56(4), 931–954.
    Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.
    Rubin, D. B. (2005). Causal inference using potential outcomes. Journal of the American Statistical Association, 100(469), 322–331.
    Spence, M. (2002). Signaling in retrospect and the informational structure of markets. American Economic Review, 92(3), 434–459.
    Sviokla, J.(2009). Forget Citibank – Borrow from Bob, in Breakthrough Ideas for 2009. Harvard Business Review 87(2), 19–40.
    Description: 碩士
    國立政治大學
    資訊管理學系
    108356033
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0108356033
    Data Type: thesis
    DOI: 10.6814/NCCU202101067
    Appears in Collections:[Department of MIS] Theses

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