Abstract
Research has demonstrated that emotions and general mood significantly influenceproductivity. This study aims to determine whether aligning tasks with an individual’s affective state can lead to greater productivity and more efficient work.
It explores the potential for integrating affective computing into task management. The developed system, affectodo, utilizes physiological data, such as heart
rate and electrodermal activity, alongside self-assessment tools, to understand the
user’s emotional state and recommend tasks that align with their current energy.
By incorporating these inputs into a rule-based algorithm, which is based on the
Yerkes-Dodson Law and the Broaden-and-Build Theory of Positive Emotions, the
system can suggest tasks that users are more likely to complete successfully.
The project involved the design, development, and testing of a prototype to examine whether managing tasks based on emotional states could reduce unfinished
tasks and improve perceived productivity. Findings from an experimental study (n
= 5), conducted within a within-subjects design framework, suggest that aligning
tasks with affective states enhances task completion rates. Future advancements
could involve integrating additional features, such as mobile functionality, and
refining the algorithm to provide more precise and effective task suggestions.
| Date of Award | 2024 |
|---|---|
| Original language | English (American) |
| Supervisor | Werner Christian Kurschl (Supervisor) |