近年來，越來越多企業與組織關注群眾募資平台，將其作為籌集資金的可能橋樑，能讓不易取得傳統銀行或風險資本的初創和小型企業，得以獲得金流投資支持。根據Absolute Reports統計，全球群眾募資市場於2021年達133.4億美元的規模，且預計至2028年將成長至288億美元，其中又以獎勵式群眾募資形式為主流。
縱使群眾募資市場迅速擴大，但募資至目標金額仍屬巨大挑戰，根據群眾募資網站統計，僅約40% 的專案成功達成募資目標。因此若能研究影響群眾募資專案的募資成功之特徵，並建立其預測模型，將能為發起者提供專案的優化建議，進而投入營運資源以增加專案的成功機率。
過往文獻多聚焦於建立最終群眾募資專案是否募資達標成功的預測模型，但專案於可投資時間內的金額累積是個動態過程，支持者於不同時間階段可能受不同的專案特徵影響，因此本研究目標為探討與分析專案於不同群眾募資階段時，能協助其「成功到達至下一階段」的關鍵特徵。以全球最大的群眾募資平台Kickstarter作資料來源，並以群眾募資專案過程三階段模型為基礎，分析發起者資訊、發起者之社交網絡、專案資訊、社交互動與專案內容與品質特徵。後續應用WOA演算法於特徵選擇，並以二元分類演算法如KNN驗證其分類準確度，以得出各橫跨至下個階段之模型，而模型所使用的特徵子集即可解釋為該階段的關鍵成功特徵，並接續應用Logistic regression了解各特徵對於專案階段性成功的正負影響程度。
經實證研究結果發現，各階段的關鍵成功特徵個數分別為18、3與2個，成功地揭示了到達各階段的關鍵成功特徵與其對專案階段性成功機率的正負影響程度，一方面填補過去研究中的知識空白，能夠更全面地理解群眾募資於不同階段的動態特徵，另一方面能更有效且更針對性地提供專案發起者於各階段的營運成功指導。

In recent years, crowdfunding has emerged as a crucial fundraising tool, particularly for startups and small businesses lacking traditional banking or venture capital access. According to Absolute Reports, the global crowdfunding market reached a scale of $13.34 billion in 2021 and is projected to grow to $28.8 billion by 2028, with reward-based crowdfunding being the dominant form.
Despite the rapid growth of crowdfunding, achieving funding goals remains a significant challenge, with only around 40% of projects succeeding on Kickstarter.com. Previous studies have primarily focused on developing predictive models to determine overall crowdfunding success. However, the process of accumulating funds during the investment period is dynamic, and supporters may be influenced by different project characteristics at various stages.
This study aims to investigate the crucial characteristics that contribute to successful progression between crowdfunding stages. By utilizing data from Kickstarter.com and employing a three-stage model, the analysis encompasses initiator information, social networks, project details, social interactions, and content quality. The feature selection process employs the WOA algorithm, with KNN serving as the binary classification algorithm for model validation. Logistic regression evaluates the impact of each feature on stage-specific success.
The empirical research findings identify 18, 3, and 2 key success features for each stage, effectively bridging gaps in knowledge and providing comprehensive insights into the dynamic nature of crowdfunding. The findings offer valuable operational guidance for project initiators, increasing their chances of success at each stage.

AbsoluteReports. (2022). Crowdfunding Market Growth (Status and Outlook) 2022-2028. A. R. P. Ltd. https://finance.yahoo.com/news/crowdfunding-market-growth-status-outlook-190200147.html
Agrawal, A. K., Catalini, C., & Goldfarb, A. (2011). The geography of crowdfunding.
Ahmad, F. S., Tyagi, D., & Kaur, S. (2017). Predicting crowdfunding success with optimally weighted random forests. 2017 International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions)(ICTUS),
Alhussan, A. A., Khafaga, D. S., El-Kenawy, E.-S. M., Ibrahim, A., Eid, M. M., & Abdelhamid, A. A. (2022). Pothole and Plain Road Classification Using Adaptive Mutation Dipper Throated Optimization and Transfer Learning for Self Driving Cars. IEEE Access, 10, 84188-84211.
Barbi, M., & Bigelli, M. (2017). Crowdfunding practices in and outside the US. Research in International Business and Finance, 42, 208-223.
Belleflamme, P., Lambert, T., & Schwienbacher, A. (2014). Crowdfunding: Tapping the right crowd. Journal of business venturing, 29(5), 585-609.
Bi, S., Liu, Z., & Usman, K. (2017). The influence of online information on investing decisions of reward-based crowdfunding. Journal of Business Research, 71, 10-18.
Borg, A., & Boldt, M. (2020). Using VADER sentiment and SVM for predicting customer response sentiment. Expert Systems with Applications, 162, 113746.
Buttice, V., Colombo, M. G., & Wright, M. (2017). Serial crowdfunding, social capital, and project success. Entrepreneurship Theory and Practice, 41(2), 183-207.
Cambridge, U. o. (2020). Cambridge Alternative Finance Benchmarks. University of Cambridge. https://ccaf.io/cafb/methodology
Cardon, M. S., Sudek, R., & Mitteness, C. (2009). The impact of perceived entrepreneurial passion on angel investing. Frontiers of entrepreneurship research, 29(2), 1.
Chen, L.-S., & Shen, E.-L. (2019). Finding the keywords affecting the success of crowdfunding projects. 2019 IEEE 6th International Conference on Industrial Engineering and Applications (ICIEA),
Chen, P.-Y., Wu, S.-y., & Yoon, J. (2004). The impact of online recommendations and consumer feedback on sales.
Chen, S.-Y., Chen, C.-N., Chen, Y.-R., Yang, C.-W., Lin, W.-C., & Wei, C.-P. (2015). Will your project get the green light? Predicting the success of crowdfunding campaigns.
Chen, X.-P., Yao, X., & Kotha, S. (2009). Entrepreneur passion and preparedness in business plan presentations: a persuasion analysis of venture capitalists' funding decisions. Academy of Management journal, 52(1), 199-214.
Cho, H., Kim, S., Lee, J., & Lee, J.-S. (2014). Data-driven integration of multiple sentiment dictionaries for lexicon-based sentiment classification of product reviews. Knowledge-Based Systems, 71, 61-71.
Cryder, C. E., Loewenstein, G., & Seltman, H. (2013). Goal gradient in helping behavior. Journal of Experimental Social Psychology, 49(6), 1078-1083.
Cui, G., Lui, H.-K., & Guo, X. (2012). The effect of online consumer reviews on new product sales. International Journal of Electronic Commerce, 17(1), 39-58.
Cumming, D. J., Leboeuf, G., & Schwienbacher, A. (2020). Crowdfunding models: Keep‐it‐all vs. all‐or‐nothing. Financial Management, 49(2), 331-360.
Davies, W. E., & Giovannetti, E. (2018). Signalling experience & reciprocity to temper asymmetric information in crowdfunding evidence from 10,000 projects. Technological Forecasting and Social Change, 133, 118-131.
Devi, S. G., & Sabrigiriraj, M. (2018). Feature selection, online feature selection techniques for big data classification:-a review. 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT),
Emary, E., Zawbaa, H. M., & Hassanien, A. E. (2016). Binary ant lion approaches for feature selection. Neurocomputing, 213, 54-65.
Forman, C., Ghose, A., & Wiesenfeld, B. (2008). Examining the relationship between reviews and sales: The role of reviewer identity disclosure in electronic markets. Information systems research, 19(3), 291-313.
Frydrych, D., Bock, A. J., Kinder, T., & Koeck, B. (2014). Exploring entrepreneurial legitimacy in reward-based crowdfunding. Venture capital, 16(3), 247-269.
Gallemore, C., Nielsen, K. R., & Jespersen, K. (2019). The uneven geography of crowdfunding success: Spatial capital on Indiegogo. Environment and Planning A: Economy and Space, 51(6), 1389-1406.
Gao, Q., & Lin, M. (2013). Linguistic features and peer-to-peer loan quality: a machine learning approach. Available at SSRN, 2446114.
Greenberg, M. D., Pardo, B., Hariharan, K., & Gerber, E. (2013). Crowdfunding support tools: predicting success & failure. In CHI'13 extended abstracts on human factors in computing systems (pp. 1815-1820).
Guo, G., Wang, H., Bell, D., Bi, Y., & Greer, K. (2003). KNN model-based approach in classification. OTM Confederated International Conferences" On the Move to Meaningful Internet Systems",
Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of machine learning research, 3(Mar), 1157-1182.
He, W., Tian, X., Tao, R., Zhang, W., Yan, G., & Akula, V. (2017). Application of social media analytics: A case of analyzing online hotel reviews. Online Information Review.
Hull, C. L. (1932). The goal-gradient hypothesis and maze learning. Psychological review, 39(1), 25.
Hutto, C., & Gilbert, E. (2014). Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the international AAAI conference on web and social media,
Jain, A. (2021). The Top 10 Crowdfunding Platforms. Entrepreneur. https://www.entrepreneur.com/business-news/these-are-the-top-10-crowdfunding-platforms/366972
Janků, J., Kučerová, Z., & Dařena, F. (2019). Behavioural Insights from Crowdfunding Financing: Power of Nudges. INFINITI Conference on International Finance,
Kaminski, J. C., & Hopp, C. (2020). Predicting outcomes in crowdfunding campaigns with textual, visual, and linguistic signals. Small Business Economics, 55(3), 627-649.
Kaur, H., & Gera, J. (2017). Effect of social media connectivity on success of crowdfunding campaigns. Procedia Computer Science, 122, 767-774.
Kickstarter. (2023). Kickstarter Stats. Kickstarter,. https://www.kickstarter.com/help/stats#:~:text=Funding%20on%20Kickstarter%20is%20all,their%20goal%20were%20successfully%20funded.
Kim, T., Por, M. H., & Yang, S.-B. (2017). Winning the crowd in online fundraising platforms: The roles of founder and project features. Electronic Commerce Research and Applications, 25, 86-94.
Kivetz, R., Urminsky, O., & Zheng, Y. (2006). The goal-gradient hypothesis resurrected: Purchase acceleration, illusionary goal progress, and customer retention. Journal of marketing research, 43(1), 39-58.
Kraus, S., Richter, C., Brem, A., Cheng, C.-F., & Chang, M.-L. (2016). Strategies for reward-based crowdfunding campaigns. Journal of Innovation & Knowledge, 1(1), 13-23.
Kuppuswamy, V., & Bayus, B. L. (2018). Crowdfunding creative ideas: The dynamics of project backers. In The economics of crowdfunding (pp. 151-182). Springer.
Lagazio, C., & Querci, F. (2018). Exploring the multi-sided nature of crowdfunding campaign success. Journal of Business Research, 90, 318-324.
Lee, E.-J., & Shin, S. Y. (2014). When do consumers buy online product reviews? Effects of review quality, product type, and reviewer’s photo. Computers in human behavior, 31, 356-366.
Li, F. (2008). Annual report readability, current earnings, and earnings persistence. Journal of Accounting and economics, 45(2-3), 221-247.
Li, H., Chen, X., Zhang, Y., & Hai, M. (2018). Prediction of financing goal of crowdfunding projects. Procedia Computer Science, 139, 108-113.
Li, Y., Rakesh, V., & Reddy, C. K. (2016). Project success prediction in crowdfunding environments. Proceedings of the Ninth ACM International Conference on Web Search and Data Mining,
Liang, X., Hu, X., & Jiang, J. (2020). Research on the effects of information description on crowdfunding success within a sustainable economy—the perspective of information communication. Sustainability, 12(2), 650.
Mafarja, M., & Mirjalili, S. (2018). Whale optimization approaches for wrapper feature selection. Applied Soft Computing, 62, 441-453.
Mafarja, M. M., & Mirjalili, S. (2017). Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing, 260, 302-312.
Market Data Forcast. (Jan, 2022). Global Crowdfunding Market Research Report – Segmentation by Product (Awards-Based Crowdfunding, Crowdfunding Auctions, and others), End-users (Cultural Industries, Technology, Product, Healthcare, Others), Industry Analysis, Size, Share, Growth, Trends & Forecast To 2027. Market Data Forcast,. https://www.marketdataforecast.com/market-reports/crowdfunding-market
Mirjalili, S. (2015). The ant lion optimizer. Advances in engineering software, 83, 80-98.
Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, 95, 51-67.
Mollick, E. (2014). The dynamics of crowdfunding: An exploratory study. Journal of business venturing, 29(1), 1-16.
Moritz, A., & Block, J. H. (2016). Crowdfunding: A literature review and research directions. Crowdfunding in Europe, 25-53.
Moy, N., Chan, H. F., & Torgler, B. (2018). How much is too much? The effects of information quantity on crowdfunding performance. PloS one, 13(3), e0192012.
Nielsen, N. (2015). Global trust in advertising. NY: USA, Nielsen Media Research, ACNielsen. Retrieved on April, 3, 2018.
Ordanini, A., Miceli, L., Pizzetti, M., & Parasuraman, A. (2011). Crowd‐funding: transforming customers into investors through innovative service platforms. Journal of service management.
Petitjean, M. (2018). What explains the success of reward-based crowdfunding campaigns as they unfold? Evidence from the French crowdfunding platform KissKissBankBank. Finance Research Letters, 26, 9-14.
Petruzzelli, A. M., Natalicchio, A., Panniello, U., & Roma, P. (2019). Understanding the crowdfunding phenomenon and its implications for sustainability. Technological Forecasting and Social Change, 141, 138-148.
Randall, N., Šabanović, S., Milojević, S., & Gupta, A. (2022). Top of the class: mining product characteristics associated with crowdfunding success and failure of home robots. International Journal of Social Robotics, 14(1), 149-163.
Robiady, N. D., Windasari, N. A., & Nita, A. (2021). Customer engagement in online social crowdfunding: The influence of storytelling technique on donation performance. International journal of research in marketing, 38(2), 492-500.
Ryoba, M. J., Qu, S., Ji, Y., & Qu, D. (2020). The right time for crowd communication during campaigns for sustainable success of crowdfunding: Evidence from Kickstarter platform. Sustainability, 12(18), 7642.
Ryoba, M. J., Qu, S., & Zhou, Y. (2021). Feature subset selection for predicting the success of crowdfunding project campaigns. Electronic Markets, 31(3), 671-684.
Schwienbacher, A., & Larralde, B. (2010). Crowdfunding of small entrepreneurial ventures. Handbook of entrepreneurial finance, Oxford University Press, Forthcoming.
Short, J. C., Ketchen Jr, D. J., McKenny, A. F., Allison, T. H., & Ireland, R. D. (2017). Research on crowdfunding: Reviewing the (very recent) past and celebrating the present. Entrepreneurship Theory and Practice, 41(2), 149-160.
Small, D. A., & Verrochi, N. M. (2009). The face of need: Facial emotion expression on charity advertisements. Journal of marketing research, 46(6), 777-787.
Song, Y., Berger, R., Yosipof, A., & Barnes, B. R. (2019). Mining and investigating the factors influencing crowdfunding success. Technological Forecasting and Social Change, 148, 119723.
Talbi, E.-G. (2009). Metaheuristics: from design to implementation. John Wiley & Sons.
Turney, P. D., & Littman, M. L. (2002). Unsupervised learning of semantic orientation from a hundred-billion-word corpus. arXiv preprint cs/0212012.
Utz, S., Kerkhof, P., & Van Den Bos, J. (2012). Consumers rule: How consumer reviews influence perceived trustworthiness of online stores. Electronic Commerce Research and Applications, 11(1), 49-58.
Vitaglione, G. D., & Barnett, M. A. (2003). Assessing a new dimension of empathy: Empathic anger as a predictor of helping and punishing desires. Motivation and Emotion, 27, 301-325.
Wang, N., Li, Q., Liang, H., Ye, T., & Ge, S. (2018). Understanding the importance of interaction between creators and backers in crowdfunding success. Electronic Commerce Research and Applications, 27, 106-117.
Wang, N., Liang, H., Ge, S., & Xue, Y. (2015). How to crowdfund more: a signaling perspective.
Wang, W., Zhu, K., Wang, H., & Wu, Y.-C. J. (2017). The impact of sentiment orientations on successful crowdfunding campaigns through text analytics. IET Software, 11(5), 229-238.
Ward, C., & Ramachandran, V. (2010). Crowdfunding the next hit: Microfunding online experience goods. Workshop on computational social science and the wisdom of crowds at NIPS2010,
Westerlund, M., Singh, I., Rajahonka, M., & Leminen, S. (2019). Can short-text project summaries predict funding success on crowdfunding platforms? ISPIM Conference Proceedings,
Xu, K., & Wang, X. (2020). “Kindhearted people, please save my family”: narrative strategies for new media medical crowdfunding. Health Communication, 35(13), 1605-1613.
Xu, L. Z. (2018). Will a digital camera cure your sick puppy? Modality and category effects in donation-based crowdfunding. Telematics and Informatics, 35(7), 1914-1924.
Yin, C., Liu, L., & Mirkovski, K. (2019). Does more crowd participation bring more value to crowdfunding projects? The perspective of crowd capital. Internet Research.
Yuan, H., Lau, R. Y., & Xu, W. (2016). The determinants of crowdfunding success: A semantic text analytics approach. Decision Support Systems, 91, 67-76.
Zhang, F., Xue, B., Li, Y., Li, H., & Liu, Q. (2021). Effect of textual features on the success of medical crowdfunding: model development and econometric analysis from the tencent charity platform. Journal of medical Internet research, 23(6), e22395.
Zhang, H., & Chen, W. (2019). Crowdfunding technological innovations: Interaction between consumer benefits and rewards. Technovation, 84, 11-20.
Zhao, J., Zhang, C., & Yao, F. (2020). Efficient selection of visual features in automatic image retrieval. 2020 12th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA),
Zhou, M. J., Lu, B., Fan, W. P., & Wang, G. A. (2018). Project description and crowdfunding success: an exploratory study. Information Systems Frontiers, 20(2), 259-274.
Ziegler, T., Shneor, R., Wenzlaff, K., Odorović, A., Hao, R., & Ryll, L. (2021). The 4th European alternative finance benchmarking report. Available at SSRN 3772260.