Exploring the Impacts of HEXACO Personality Traits on Text Composition and Transcription

要旨

This study investigates the relationship between the HEXACO personality traits and text entry behaviors in composition and transcription tasks. By analyzing metrics such as entry speed, accuracy, editing efforts, and readability, we identified correlations between specific traits and text entry performance. In composition, honesty-humility and agreeableness were the strongest predictors, correlating significantly with composition time, text length, and editing efforts. In transcription, openness, honesty-humility, and agreeableness influenced performance, though no single trait consistently predicted all metrics. Interestingly, extraversion did not show strong correlations in either task, despite its established link to composition performance in academic contexts. These findings suggest that personality traits affect text entry behavior differently depending on the task, with creative tasks like composition being shaped by distinct traits compared to repetitive tasks like transcription. This research provides valuable insights into the relationship between personality and text entry, opening avenues for personalizing interaction systems based on individual traits.

著者
Jannatul Ferdous Srabonee
University of California, Merced, Merced, California, United States
Ohoud Mosa. Alharbi
King Saud University, Riyadh, Riyadh, Saudi Arabia
Wolfgang Stuerzlinger
Simon Fraser University, Vancouver, British Columbia, Canada
Ahmed Sabbir. Arif
University of California, Merced, Merced, California, United States
DOI

10.1145/3706598.3714149

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714149

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Innovations in Interaction Design

Annex Hall F206
7 件の発表
2025-04-29 20:10:00
2025-04-29 21:40:00
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