Qualitative Framing of Financial Incentives - A Case of Emotion Annotation

TL;DR

This study investigates how qualitative framing of financial incentives influences worker performance on online labor platforms, finding that well-formulated qualitative incentives improve performance especially on difficult tasks.

Contribution

The paper introduces a novel approach of using qualitative framing for financial incentives and demonstrates its effectiveness in crowdsourcing tasks with varying difficulty levels.

Findings

Qualitative framing improves performance on hard tasks.

Well-formulated incentives increase accuracy and effort.

Effectiveness depends on task difficulty.

Abstract

Online labor platforms, such as the Amazon Mechanical Turk, provide an effective framework for eliciting responses to judgment tasks. Previous work has shown that workers respond best to financial incentives, especially to extra bonuses. However, most of the tested incentives involve describing the bonus conditions in formulas instead of plain English. We believe that different incentives given in English (or in qualitative framing) will result in differences in workers' performance, especially when task difficulties vary. In this paper, we report the preliminary results of a crowdsourcing experiment comparing workers' performance using only qualitative framings of financial incentives. Our results demonstrate a significant increase in workers' performance using a specific well-formulated qualitative framing inspired by the Peer Truth Serum. This positive effect is observed only when the difficulty of the task is high, while when the task is easy there is no difference of which incentives to use.