User Behaviors, User Engagement, and Consequences

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Paper Type

Completed

Paper Number

2433

Description

Prior studies of online crowdsourcing platforms have examined participants’ behaviors and found that experienced solvers strategically time their submissions and are more likely to win a contest. But what gives experienced solvers an edge in these contests is not well understood. Our study seeks to understand what differentiates experienced solvers, with a particular focus on how they leverage information in open design contests. We use large-scale empirical analysis employing deep-learning algorithms and find that, while experienced solvers are similar to less-experienced solvers in a number of ways, experienced solvers are more adept at integrating information from prior highly-rated submissions from other solvers in a contest. We find that experienced solvers whose submissions are closer in similarity to a synthesized image of highly-rated prior submissions, are more likely to win. Our findings provide new insights into the winning strategies of experienced solvers and have implications for the design of such markets.

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Dec 14th, 12:00 AM

Synthesizing Winning Strategies: What Differentiates Experienced Solvers in Crowdsourcing Markets?

Prior studies of online crowdsourcing platforms have examined participants’ behaviors and found that experienced solvers strategically time their submissions and are more likely to win a contest. But what gives experienced solvers an edge in these contests is not well understood. Our study seeks to understand what differentiates experienced solvers, with a particular focus on how they leverage information in open design contests. We use large-scale empirical analysis employing deep-learning algorithms and find that, while experienced solvers are similar to less-experienced solvers in a number of ways, experienced solvers are more adept at integrating information from prior highly-rated submissions from other solvers in a contest. We find that experienced solvers whose submissions are closer in similarity to a synthesized image of highly-rated prior submissions, are more likely to win. Our findings provide new insights into the winning strategies of experienced solvers and have implications for the design of such markets.

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