In Crowdfunding platforms, people turn their prototype ideas into real products by raising money from the crowd, or invest in someone else's projects. In reward-based crowdfunding platforms such as Kickstarter and Indiegogo, selecting accurate reward delivery duration becomes crucial for creators, backers, and platform providers to keep the trust between the creators and the backers, and the trust between the platform providers and users. According to Kickstarter, 35 backers did not receive rewards on time. Unfortunately, little is known about on-time and late reward delivery projects, and there is no prior work to estimate reward delivery duration. To fill the gap, in this paper, we (i) extract novel features that reveal latent difficulty levels of project rewards; (ii) build predictive models to identify whether a creator will deliver all rewards in a project on time or not; and (iii) build a regression model to estimate accurate reward delivery duration (i.e., how long it will take to produce and deliver all the rewards). Experimental results show that our models achieve good performance -- 82.5 accuracy, 78.1 RMSE, and 0.108 NRMSE at the first 5 of the longest reward delivery duration.
Recently, @cite_28 interviewed crowdfunding participants and found various factors which influenced backers' trust. They also conducted analysis for 4,089 delayed projects to understand how 8 factors were related to delayed duration. However, they did not study which project will pass estimated delivery date, nor how long a reward delivery including production will take.
@cite_28
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Trust is essential for beginning and maintaining relationships online where assessing uncertainties and risks is difficult. While product delays have been shown to reduce trust in e-commerce settings, we understand little about the effect of delays on trust in the increasingly popular context of crowdfunding. In a mixed method study, we examine what factors influence backers' trust in crowdfunding when rewards are delayed. Based on in-depth interviews with crowdfunding participants, we found that a rich set of factors influenced backers' trust including backers' role identity and domain knowledge, backer's research on a creator's background, creators' communication during delays, and duration of delays. To better understand the factors affecting delays, we conducted a regression analysis with 4,089 delayed projects and found that the funding goal, number of backers, percent raised, number of reward levels, and creator's previous crowdfunding experience are associated with the duration of delay. We discuss design implications for managing delays and maintaining trust in crowdfunding. Entrepreneurs are turning to crowdfunding as a way to finance their creative ideas. Crowdfunding involves relatively small contributions of many consumer-investors over a fixed time period (generally a few weeks). The purpose of this paper is to add to our empirical understanding of backer dynamics over the project funding cycle. Two years of publicly available data on projects listed on Kickstarter is used to establish that the typical pattern of project support is U-shaped — in general, backers are more likely to contribute to a project in the first and last week as compared to the middle period of the funding cycle. We further establish that this U-shape pattern of support is pervasive across projects, including both successfully and unsuccessfully funded projects, those with large and small goals, and projects in different categories. We then empirically explore the dynamics associated with several factors, including collective attention effects from platform sorting options, the role of family and friends in supporting projects, the effects of social influence, and the role of project updates over the project funding cycle. Crowdfunding has gained widespread attention in recent years. Despite the huge success of crowdfunding platforms, the percentage of projects that succeed in achieving their desired goal amount is only around 40 . Moreover, many of these crowdfunding platforms follow "all-or-nothing" policy which means the pledged amount is collected only if the goal is reached within a certain predefined time duration. Hence, estimating the probability of success for a project is one of the most important research challenges in the crowdfunding domain. To predict the project success, there is a need for new prediction models that can potentially combine the power of both classification (which incorporate both successful and failed projects) and regression (for estimating the time for success). In this paper, we formulate the project success prediction as a survival analysis problem and apply the censored regression approach where one can perform regression in the presence of partial information. We rigorously study the project success time distribution of crowdfunding data and show that the logistic and log-logistic distributions are a natural choice for learning from such data. We investigate various censored regression models using comprehensive data of 18K Kickstarter (a popular crowdfunding platform) projects and 116K corresponding tweets collected from Twitter. We show that the models that take complete advantage of both the successful and failed projects during the training phase will perform significantly better at predicting the success of future projects compared to the ones that only use the successful projects. We provide a rigorous evaluation on many sets of relevant features and show that adding few temporal features that are obtained at the project's early stages can dramatically improve the performance. Crowdfunding websites such as Kickstarter are becoming increasingly popular, allowing project creators to raise hundreds of millions of dollars every year. However, only one out of two Kickstarter campaigns reaches its funding goal and is successful. It is therefore of prime importance, both for project creators and backers, to be able to know which campaigns are likely to succeed. We propose a method for predicting the success of Kickstarter campaigns by using both direct information and social features. We introduce a first set of predictors that uses the time series of money pledges to classify campaigns as probable success or failure and a second set that uses information gathered from tweets and Kickstarter's projects backers graph. We show that even though the predictors that are based solely on the amount of money pledged reach a high accuracy, combining them with predictors using social features enables us to improve the performance significantly. In particular, only 4 hours after the launch of a campaign, the combined predictor reaches an accuracy of more than 76 (a relative improvement of 4 ).