【成果速递】Matchmaking in reward-based crowdfunding platforms

1.Shaojian Qu

Prof. Shaojian Qu received the Ph.D. degree in operations research from Xi’an Jiao tong University, Xi’an, China, in 2008. He was a Postdoctoral Fellow with the National University of Singapore, Singapore, and the Harbin Institute of Technology, Harbin, China, in 2011. He is currently a Professor with the School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, China.

2.Lei Xu

Prof. Lei Xu received the Ph.D. degree in logistics and supply chain management from Nankai University, Tianjin, China, in 2012. He is currently a Full Professor with the Civil Aviation University of China, Tianjin. From January 2012 to August 2012, he visited as a Visiting Scholar with the Hong Kong Polytechnic University, Hong Kong. From 2020, he has been named as the Highcited Scholar by Elsevier.

3.Sachin Kumar Mangla

Dr. Sachin Kumar Mangla is working in the field of Green and Sustainable Supply Chain and Operations; Industry 4.0; Circular Economy; Decision Making and Simulation. He has a teaching experience of more than five years in Supply Chain and Operations Management and Decision Making, and currently associated in teaching with various universities in U.K., Turkey, India, China, France, etc. He is committed to do and promote high quality research.

4.Felix Chan

Prof. Felix Chan received his B.Sc. Degree in Mechanical Engineering from Brighton University, U.K., and obtained his M.Sc. and Ph.D. in Manufacturing Engineering from the Imperial College of Science and Technology, University of London, U.K. Prior joining Macau University of Science and Technology, Prof. Chan has many years of working experience in other universities including The Hong Kong Polytechnic University; University of Hong Kong; University of South Australia; University of Strathclyde.

5.Jianli Zhu

Jianli Zhu, born in 1996, M. S. candidate with University of Shanghai for Science and Technology. His research interests include supply chain management, Internet finance.

6.Sobhan Sean Arisian

Dr. Sobhan Sean Arisian is an applied operations researcher with special interests in modelling and solving complex Logistics and Supply Chain Management problems. Sean is the Editorial Advisory Board (EAB) member of ‘Transportation Research Part E’ and ‘Logistics’ journal, and Associate Editor of ‘Modern Supply Chain Research and Application’ journal.

数字化的快速发展丰富了资本市场的层次结构。在初创企业融资领域，可通过互联网直接获得公共资金支持的新型金融科技解决方案的出现导致了多边众筹平台的兴起。如今，众筹已成为金融领域平台经济最重要的组成部分之一。传统的聚类方法无法准确地对支持者的特征向量进行聚类，也无法将潜在的支持者匹配到兼容的众筹项目。本文使用 Apriori 算法结合其他机器学习工具对潜在支持者进行聚类，并为众筹项目提供更准确的推荐。着眼于主要奖励型众筹平台上列出的潜在项目，首先从可用支持者列表中获得的数据。然后利用Apriori算法得到不同项目支持者之间的关联度，并根据支持者的关联度进行支持者的权重计算，关联度作为关键指标对相似的支持者进行聚类。

The rapid development of analytics and digitisation has enriched the hierarchy of the capital market. In the field of start-up financing, the emergence of new financial technology solutions that can directly secure public funding support through the Internet has led to the rise ofmulti-sided crowdfunding platforms. Nowadays,crowdfunding has become one of the mos timportant components ofthe platformeconomy in the financial field.

Compared with traditional financing, crowdfunding is more accessible and flexible, and it provides more opportunities for individuals and enterprises to raise funds from crowds. Despite its advantages, reward-based crowdfunding is known to have a low success rate. The performance ofKickstarter, the largest crowdfunding platform, has been declining since its launch in 2009.

Over the past decade, many researchers have studied the factors that influence the success of crowdfunding projects. In this stream of study, an increasing number of researchers have begun to examine how to reduce the failure rate of crowdfunding projects. In general, most backers have their own preferences for crowdfunding projects. Meanwhile, despite the relatively mature research on the success of crowdfunding, only a few studies have examined how suitable crowdfunding products/projects can be effectively matched with the right backers to enhance the success rate of crowdfunding.

In this paper, authors use the Apriori algorithm in conjunction with other machine learning tools to cluster the potential backers and provide more accurate recommendations for crowdfunding projects. Focusing on potential projects listed in a major reward-based crowdfund-ing platform, authors first train the data obtained from the available list of backers. Using the Apriori algorithm, the degree of association between different project backers is then obtained, and weight calculation of the backers is carried out according to the association degree of the backers. The degree of association is used as a key index to cluster similar backers.

Finally, authors test the model and determine whether clustering can correctly classify the data in the test set based on the Apriori algorithm. Our experimental results show that there is 90% accuracy, precision and recall of the model. The proposed solution outperforms the other five benchmark methods and offers an imporved matchmaking by connecting the listed crowdfunding projects to the right backers.

Fig.1 The basic flow of the model.

Theoretical insights:

(1) A novel clustering method is developed and demonstrated for clustering complex crowdfunding projects. The quality of clustering methods (Abbasimehr and Sheikh Baghery 2022) affects the clustering effect of complex crowdfunding projects. Our research applies the Apriori algorithm to improve the quality of clustering and product recommendations in complex crowdfunding ecosystems.

(2) The previous literature on clustering (Du, Li, and Wang 2019; Geiger and Moore 2022; McSweeney et al. 2022) undermines the importance of the simultaneous consideration of the behaviour characteristics of backers and future crowdfunding projects. This research proposes the association rule-based clustering method for backers by considering backers’ behavioural characteristics and future crowdfunding projects simultaneously.

(3) This study combines different machine learning methods to optimise clustering algorithms. The results show that the Apriori algorithm and other machine learning tools are able to improve the performance of the proposed clustering method for crowdfunding projects.

(4) The comparative analysis reveals the advantages of the proposed model. Our findings showed that the degree of association rule-based model for crowdfunding backers outperforms the other five existing benchmark methods.

Managerial insights:

(1) It is very important to investigate behaviour characteristics (Vanpoucke and Vereecke 2010) and uncertainty (Qu, Li, and Ji 2021) in decision science. The behavioural dimensions of backers and the sensitivity of crowdfunding projects cannot be overlooked during clustering. Therefore, the proposed model enables decision makers to cluster backers by considering their behavioural  characteristics and the uncertainties of crowdfunding projects concurrently.

(2) In the field of product recommendation, Wang (2015) studied the recommendations of smart phones and wearable devices. Wu et al. (2021) developed a knowledge recommendation system for industrial products and illustrated the performance of the model in a crane case. However, few studies have applied product recommendation to the field of crowdfunding. Decision makers can use the proposed method for effective recommendations of crowdfunding products.

(3) Clustering is particularly important in simulation analysis. However, it is often difficult to obtain accurate clustering results. The most effective methods include association rules (Buddhakulsomsiri et al. 2006) and machine learning (Yeou-Ren, Ruey-Shiang, and Ken-Chun 2012). Decision makers can cluster backers more accurately and effectively using the proposed model that combines association rules and machine learning.

(4) The accuracy of the proposed model is relatively high, and the model can achieve good results in crowdfunding. Using the developed model based on the Apriori algorithm, managers can achieve 90% accuracy in relating suitable crowdfunding projects to the right backers.

This study utilised clustering based on the Apriori clustering algorithm, which further enhances the crowdfunding products’ success rate. We collected data from crowdfunding websites, analysed the association rules of relevant backers, and then clustered similar backers. Unlike in cases that use traditional clustering algorithms, we could not take advantage of the behaviour information of backers to describe the specific value of the clustering algorithm. This problem was reflected in the data of the backers’ behaviour records on the platform. We overcame this problem by utilising the Apriori algorithm of machine learning to analyse the association rules between backers. We also calculated the correlation strength coefficients amongst different backers and performed cluster analysis.

The proposed method was proved to perform well and able to cluster the same backers correctly. This method enables us to recommend crowdfunding projects for different backers. It also optimises the traditional clustering algorithms and clusters the same backers according to their behaviours. Using this model, we can predict backers with the most decisive relevance and identify the kinds of projects that they could support next time on the basis of their successful projects. From the perspective of the backers, they can achieve their own value to a large extent and invest where they can get the best return. For creators, their crowdfunding projects’ success rate can be improved, and they can thus generate higher profits.

Similar to any other research, this study has several shortcomings. Hence, developing new methods and comparing them with the presented model, testing the models with larger datasets, and conducting an empirical analysis to investigate the improved success rate of crowdfunding projects (as a result of the new product recommendation system) are suggested as possible directions for future research.

https://www.tandfonline.com/doi/full/10.1080/00207543.2022.2121870