Time Estimation Bias in Knowledge Work: Tasks With Fewer Time Constraints Are More Error-Prone
| Autor: | Anna L. Cox, Duncan P. Brumby, Yoana Ahmetoglu |
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| Rok vydání: | 2020 |
| Předmět: |
Estimation
business.industry Computer science 05 social sciences 020207 software engineering 02 engineering and technology Machine learning computer.software_genre Scheduling (computing) Work (electrical) Time estimation 0202 electrical engineering electronic engineering information engineering 0501 psychology and cognitive sciences Time management Artificial intelligence Duration (project management) business computer 050107 human factors |
| Zdroj: | CHI Extended Abstracts |
| Popis: | Previous research has found that people often make time estimation errors in their daily planning at work. However, there is limited insight on the types of estimation errors found in different knowledge work tasks. This one-day diary study with 20 academics compared the tasks people aimed to achieve in the morning with what they actually did during the day. Results showed that participants were good at estimating the duration of time-constrained tasks, such as meetings, however they were biased when estimating the time they would spend on less time-constrained tasks. Particularly, the time needed for email and coding tasks was underestimated, whereas the time needed for writing research and planning activities was overestimated. The findings extend previous research by measuring in situ whether some tasks are more prone to time estimation errors than others. Planning and scheduling (AI) tools could incorporate this knowledge to help people overcome these time estimation biases in their work. |
| Databáze: | OpenAIRE |
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