Kitlelerin Gücü Adına Güç Bende Artık: Başarılı Kitle Fonlaması Projelerin Özelliklerinin Tespit Edilmesi

Kitle fonlaması, fon bulmada sıkıntı yaşayan proje sahipleri için alternatif bir finansman yöntemi olarak kendine yer edinmiştir. Proje sahipleri, kitle fonlaması platformlarına projelerini sunarak, bireylerden fon talep etmekte ve topladıkları fonlarla projelerini gerçekleştirebilmektedir. Projeler, platformda yayınlandıktan sonra proje güncellenebilir, fon sağlayanlar tarafından yorum yapılabilir ve böylelikle projenin başarı durumu etkilenebilmektedir. Bu nedenle mevcut çalışmada, kitle fonlaması projelerinin başlangıçlarında başarı durumlarının tespit edilmesi amaçlanmıştır. Literatürdeki çalışmalardan farklı olarak, sadece projenin başlangıç aşamasında, proje sahibinin değiştirebileceği sayısal değişkenler analize dahil edilmiştir. Veri setinde, ödül temelli kitle fonlaması platformu olan Kickstarter.com’a sunulan ve çalışmanın amacına uyan toplamda 4758 proje ile 8 değişken bulunmaktadır. Lojistik regresyon analizi sonuçlarına göre, kısa tanıtım uzunluğu, tanıtım uzunluğu ve video sayısının proje başarısını etkilemediği, istenen fon miktarı, sık kullanılan soru sayısı, ödül sayısı, görsel sayısı ve proje süresinin proje başarısını etkilediği tespit edilmiştir. Ayrıca kurulan modelle, projelerin başarı durumları %75.6 doğru sınıflandırılmıştır. Lojistik regresyon analizinin haricinde, t-test ve korelasyon analizleri de veriye uygulanmış ve sonuçlar yorumlanmıştır.

Anahtar Kelimeler:

Kitle fonlaması, sınıflandırma, lojistik regresyon.

By the Power of Grayskull, I Have the Power: Determining the Characteristics of Successful Crowdfunding Projects

Crowdfunding has emerged as an alternative financing method for project owners who have difficulties in finding funds. Project owners submit their projects to crowdfunding platforms, request funds from individuals and put their projects into action with the funds they collect. After the projects are published on the platform, the project can be updated, funders can make comments, and thus the project’s success can be affected. Therefore, in the present study, it is aimed to determine the success of crowdfunding projects at the beginning. Unlike the studies in the literature, only the numerical variables that the project owner can change at the beginning of the project are included in the analysis. In the dataset, there are 8 variables with a total of 4758 projects submitted to Kickstarter.com, a reward-based crowdfunding platform. According to the results of the logistic regression analysis, it was determined that description length, full description length and number of videos did not affect success of a project, while goal amount of funding, number of frequently used questions, the number of awards, the number of images and the project duration affected the success of the project. The classification rate of the proposed model was %75.6. In addition to the logistic regression analysis, t-test and correlation analyzes were also applied to the data and the results were interpreted.

Keywords:

Crowdfunding, classification, logistic regression,

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Ahmad, F. S., Tyagi, D., ve Kaur, S. (2017). Predicting crowdfunding success with optimally weighted random forests. In 2017 International Conference on Infocom Technologies and Unmanned Systems, Amnity University, Dubai.
Albayrak, A. S. (2006). Uygulamalı çok değişkenli istatistik teknikleri. Asil yayın dağıtım.
Bingöl, G., ve Türkmen, S. Y. (2016). Girişimciliğin finansmanında melek sermaye ve Türkiye Uygulaması. Öneri Dergisi, 12(45), 357-373.
Bruce, P., Bruce, A., ve Gedeck, P. (2020). Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python. ABD: O'Reilly Media.
Cecere, G., Le Guel, F., & Rochelandet, F. (2017). Crowdfunding and social influence: an empirical investigation. Applied economics, 49(57), 5802-5813.
Cordova, A., Dolci, J., ve Gianfrate, G. (2015). The determinants of crowdfunding success: evidence from technology projects. Procedia-Social and Behavioral Sciences, 181, 115-124.
Crosetto, P., ve Regner, T. (2014). Crowdfunding: Determinants of success and funding dynamics (No. 2014-035). Jena Economic Research Papers.
Diez, D. M., Barr, C. D., ve Cetinkaya-Rundel, M. (2017). OpenIntro Statistics (3rd Ed.). OpenIntro.
Gadja, O., ve Walton, J. (2013). Review of crowdfunding for development initiatives. Evidence on Demand, UK 31 pp. [DOI: http://dx.doi.org/10.12774/eod_hd061.jul2013.gadja;walton]
Giudici, P. (2003). Applied data mining: statistical methods for business and industry. England: John Wiley & Sons.
Gobble, M. M. (Ed.). (2012). Everyone is a venture capitalist: The new age of crowdfunding. Research technology management, 55(4), 4-7.
Greenberg, M. D., Pardo, B., Hariharan, K., ve Gerber, E. (2013). Crowdfunding support tools: predicting success & failure. In CHI'13 Extended Abstracts on Human Factors in Computing Systems, New York.
Guo, Y., Zhou, X., Zhan, C., Zeng, Y., ve Zhong, L. (2020). Prediction and analysis of success on crowdfunding projects. In Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering, Çin.
Hilmi, A. (2019). Archived Kickstarter Projects, https://www.kaggle.com/uysalah/archived-kickstarter-projects (Erişim tarihi: 10.08.2021)
Hou, R., Li, L., & Liu, B. (2020). Backers investment behavior on explicit and implicit factors in reward-based crowdfunding based on ELM theory. Plos one, 15(8), e0236979.
İpekten, O. B. (2006). Risk Sermayesi Finansman Modeli. Atatürk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 7(1), 385-408.
Joenssen, D., Michaelis, A., ve Müllerleile, T. (2014). A link to new product preannouncement: Success factors in crowdfunding. Available at SSRN 2476841.
Kaur, H., ve Gera, J. (2017). Effect of social media connectivity on success of crowdfunding campaigns. Procedia computer science, 122, 767-774.
Kırbıyık, Ö. F. (2018). Türk ve Amerikan Mevzuatında Kitlesel Fonlama Düzenlemeleri. Banka ve Finans Hukuku Dergisi, 7(26), 303-312.
Koch, J. A., ve Siering, M. (2015). Crowdfunding success factors: The characteristics of successfully funded projects on crowdfunding platforms.
Kunz, M. M., Englisch, O., Beck, J., ve Bretschneider, U. (2016). Sometimes you win, sometimes you learn – success factors in reward-based crowdfunding. In Multikonferenz Wirtschaftsinformatik (MKWI), Ilmenau.
Kuppuswamy, V., ve Bayus, B. L. (2018). Crowdfunding creative ideas: The dynamics of project backers. In The economics of crowdfunding (pp. 151-182). Palgrave Macmillan, Cham.
Kuti, M., ve Madarász, G. (2014). Crowdfunding. Public Finance Quarterly, 59(3), 355.
Liao, C., Zhu, Y., & Liao, X. (2015). The role of internal and external social capital in crowdfunding: Evidence from China. Revista de cercetare si interventie socialâ, 49, 187-204.
Mejia, J., Urrea, G., & Pedraza‐Martinez, A. J. (2019). Operational transparency on crowdfunding platforms: effect on donations for emergency response. Production and Operations Management, 28(7), 1773-1791.
Mitra, D. (2012). The role of crowdfunding in entrepreneurial finance. Delhi Business Review, 13(2), 67-72.
Mollick, E. (2012). The dynamics of crowdfunding: Determinants of success and failure. In Social Science Research Network (SSRN).
Morrissette, S. G. (2007). A profile of angel investors. The Journal of Private Equity, 10(3), 52-66.
Moutinho, N., ve Leite, P. M. (2013). Critical success factors in crowdfunding: the case of Kickstarter.
Nabar, R. (2020). A comparative study of machine learning models for fundraising success (Doctoral dissertation) Dublin Business School, Dublin.
Negrão, C. S. V., & Brito, J. A. F. (2021). An empirical study on the determinants of success of crowdfunding projects: kickstarter. In Multidisciplinary Approaches to Crowdfunding Platforms (pp. 1-20). IGI Global.
Paintsil, I. O., Xicang, Z., ve Abban, O. J. (2021). Predicting the Level of Crowdfunding Outcome in Africa: A Supervised Machine Learning Approach.
Panesar, A. (2019). Machine learning and AI for healthcare (pp. 1-73). Coventry, UK: Apress.
Ryoba, M. J., Qu, S., ve Zhou, Y. (2020). Feature subset selection for predicting the success of crowdfunding project campaigns. Electronic Markets, 1-14.
Sauermann, H., Franzoni, C., ve Shafi, K. (2019). Crowdfunding scientific research: Descriptive insights and correlates of funding success. PloS one, 14(1), e0208384.
Shmueli, G., Bruce, P. C., Yahav, I., Patel, N. R., ve Lichtendahl Jr, K. C. (2017). Data mining for business analytics: concepts, techniques, and applications in R. New Yersey: John Wiley & Sons
Ullah, S., ve Zhou, Y. (2020). Gender, anonymity and team: What determines crowdfunding success on Kickstarter. Journal of Risk and Financial Management, 13(4), 80.
Xiao, S., Tan, X., Dong, M., ve Qi, J. (2014). How to design your project in the online crowdfunding market? Evidence from Kickstarter.
Yu, P. F., Huang, F. M., Yang, C., Liu, Y. H., Li, Z. Y., ve Tsai, C. H. (2018). Prediction of crowdfunding project success with deep learning. In 2018 IEEE 15th international conference on e-business engineering (ICEBE) (pp. 1-8). IEEE.
Zhou, M. J., Lu, B., Fan, W. P., ve Wang, G. A. (2018). Project description and crowdfunding success: an exploratory study. Information Systems Frontiers, 20(2), 259-274.