An official website of the United States government
Offering products in the forms of menu bundles is a common practice in marketing to attract customers and maximize revenues. In crowdfunding platforms such as Kickstarter, rewards also play an important part in influencing project success. Designing rewards consisting of the appropriate items is a challenging yet crucial task for the project creators. However, prior research has not considered the strategies project creators take to offer and bundle the rewards, making it hard to study the impact of reward designs on project success. In this paper, we raise a novel research question: understanding project creators’ decisions of reward designs to level their chance to succeed. We approach this by modeling the design behavior of project creators, and identifying the behaviors that lead to project success. We propose a probabilistic generative model, Menu-Offering-Bundle (MOB) model, to capture the offering and bundling decisions of project creators based on collected data of 14K crowdfunding projects and their 149K reward bundles across a half-year period. Our proposed model is shown to capture the offering and bundling topics, outperform the baselines in predicting reward designs.We also find that the learned offering and bundling topics carry distinguishable meanings and provide insights of key factors on project success.
more »
« less
Modeling Dynamic Competition on Crowdfunding Markets
The often fierce competition on crowdfunding markets can significantly affect project success. While various factors have been considered in predicting the success of crowdfunding projects, to the best knowledge of the authors, the phenomenon of competition has not been investigated. In this paper, we study the competition on crowdfunding markets through data analysis, and propose a probabilistic generative model, Dynamic Market Competition (DMC) model, to capture the competitiveness of projects in crowdfunding. Through an empirical evaluation using the pledging history of past crowdfunding projects, our approach has shown to capture the competitiveness of projects very well, and significantly outperforms several baseline approaches in predicting the daily collected funds of crowdfunding projects, reducing errors by 31.73% to 45.14%. In addition, our analyses on the correlations between project competitiveness, project design factors, and project success indicate that highly competitive projects, while being winners under various setting of project design factors, are particularly impressive with high pledging goals and high price rewards, comparing to medium and low competitive projects. Finally, the competitiveness of projects learned by DMC is shown to be very useful in applications of predicting final success and days taken to hit pledging goal, reaching 85% accuracy and error of less than 7 days, respectively, with limited information at early pledging stage.
more »
« less
- Award ID(s):
- 1717084
- PAR ID:
- 10065399
- Date Published:
- Journal Name:
- Proceedings of the 2018 World Wide Web Conference
- Page Range / eLocation ID:
- 1815 to 1824
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
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.more » « less
-
Competitive gaming, a long-standing study context for CSCW, has recently faced criticism due to its design emphasis on competition and achievement, which is associated with adverse phenomena such as player toxicity and anxiety. Recognizing this limit, game designers have proactively made design attempts to ameliorate these unintended consequences of competitive gaming. A notable example is the All Random All Mid (ARAM) mode in League of Legends (LoL), designed to introduce casualness into competitive gaming. To understand how players experience both casualness and competitiveness, a seemingly contradictory pair, we conducted an interview study with ARAM players, finding that ARAM supports 'casual competition' through decentering competition, diversifying interpersonal dynamics, and filling gaps in player needs. We further discuss how game design and player agency co-constitute casual competition, reflect on key aspects of competitive gaming design such as diversity and fairness, and provide implications for competitive gaming design, which may help combat toxicity.more » « less
-
Attracting and retaining new developers is often at the heart of open-source project sustainability and success. Previous research found many intrinsic (or endogenous) project characteristics asso- ciated with the attractiveness of projects to new developers, but the impact of factors external to the project itself have largely been overlooked. In this work, we focus on one such external factor, a project’s labor pool, which is dened as the set of contributors active in the overall open-source ecosystem that the project could plausibly attempt to recruit from at a given time. How are the size and characteristics of the labor pool associated with a project’s attractiveness to new contributors? Through an empirical study of over 516,893 Python projects, we found that the size of the project’s labor pool, the technical skill match, and the social connection be- tween the project’s labor pool and members of the focal project all signicantly inuence the number of new developers that the focal project attracts, with the competition between projects with overlapping labor pools also playing a role. Overall, the labor pool factors add considerable explanatory power compared to models with only project-level characteristics.more » « less
-
Design decision-making under competition is a critical challenge in real-world engineering design. These challenges are compounded by bounded rationality, where cognitive limitations and imperfect information influence decision-making strategies. To address these issues, we develop a game-theoretic research platform to investigate team-based design under competition. This platform abstracts and simulates real-world competitive design scenarios through controlled experiments. It features a user-friendly interface to collect behavioral data, which supports the analysis of team and individual strategies. Additionally, we validated the platform through a pilot study, demonstrating its ability to capture realistic design features and generate meaningful insights into competitive design behaviors.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3178876.3186170
