The impact of task description linguistic style on task performance: a text mining of crowdsourcing contests

Abstract

Purpose

Existing studies on the relationship between task description and task performance are insufficient, with many studies considering description length rather than content to measure quality or only evaluating a single aspect of task performance. To address this gap, this study analyzes the linguistic styles of task descriptions from 2,545 tasks on the Taskcn.com crowdsourcing platform.

Design/methodology/approach

An empirical analysis was completed for task description language styles and task performance. The paper used text mining tool Simplified Chinese Linguistic Inquiry and Word Count to extract eight linguistic styles, namely readability, self-distancing, cognitive complexity, causality, tentative language, humanizing personal details, normative information and language intensity. And it tests the relationship between the eight language styles and task performance.

Findings

The study found that more cognitive complexity markers, tentative language, humanized details and normative information increase the quantity of submissions for a task. In addition, more humanized details and normative information in a task description improves the quality of task. Conversely, the inclusion of more causal relationships in a task description reduces the quantity of submissions. Poorer readability of the task description, less self-estrangement and higher language intensity reduces the quality of the task.

Originality/value

This study first reveals the importance of the linguistic styles used in task descriptions and provides a reference for how to attract more task solvers and achieve higher quality task performance by improving task descriptions. The research also enriches existing knowledge on the impact of linguistic styles and the applications of text mining.

Citation

Yang, K., Qi, H. and Huang, Q. (2022), "The impact of task description linguistic style on task performance: a text mining of crowdsourcing contests", Industrial Management & Data Systems, Vol. 122 No. 1, pp. 322-344. https://doi.org/10.1108/IMDS-03-2021-0178