Know When to Run: Recommendations in Crowdsourcing Contests

2018 | Management Information Systems Quarterly | Citations: 30

Authors: Mo, Jiahui; Sarkar, Sumit; Menon, Syam

Abstract: Crowdsourcing contests have emerged as an innovative way for firms to solve busi ... Expand

Abstract: Crowdsourcing contests have emerged as an innovative way for firms to solve business problems by acquiring ideas from participants external to the firm. As the number of participants on crowdsourcing contest platforms has increased, so has the number of tasks that are open at any time. This has made it difficult for solvers to identify tasks in which to participate. We present a framework to recommend tasks to solvers who wish to participate in crowdsourcing contests. The existence of competition among solvers is an important and unique aspect of this environment, and our framework considers the competition a solver would face in each open task. As winning a task depends on performance, we identify a theory of performance and reinforce it with theories from learning, motivation, and tournaments. This augmented theory of performance guides us to variables specific to crowdsourcing contests that could impact a solver’s winning probability. We use these variables as input into various probability prediction models adapted to our context, and make recommendations based on the probability or the expected payoff of the solver winning an open task. We validate our framework using data from a real crowdsourcing platform. The recommender system is shown to have the potential of improving the success rates of solvers across all abilities. Recommendations have to be made for open tasks and we find that the relative rankings of tasks at similar stages of their time lines remain remarkably consistent when the tasks close. Further, we show that deploying such a system should benefit not only the solvers, but also the seekers and the platform itself. Collapse

Topics: crowdsourcing recommender system personality logistics management intrinsic motivation

Methods: experiment support vector machine Bayesian network naïve bayes artificial neural network

Theories: performance theory

A Rule-Based Method for Determining the Degree of Student Satisfaction of a Web-Based Learning Environment

2000 | Americas Conference on Information Systems | Citations: 0

Authors: Sallis, Philip J; Masi, Catherine A

Abstract: Student essays representing their individual reflections on a collaborative web- ... Expand

Abstract: Student essays representing their individual reflections on a collaborative web-based course in International Business are computationally analyzed according to a classification scheme based on a set of a priori fuzzy categories. This classification method enables the identification of themes and trends in the student responses that can be used to illustrate an overall evaluation of the personal learning experiences for this course. By processing the classification results using a computational neural network, we can depict the clustering intensity of thematic elements and illustrate the strength of dependencies between classification attribute values topologically using a self-organizing map (SOM), which provides a pattern recognition visualization. The resulting SOM can then be used to compare successive depictions for future iterations of new thematic data from student self-evaluations. Collapse

Topics: Perl programming language systems development

Methods: artificial neural network cluster analysis computational algorithm simulation linguistic analysis