Journal article
The effects of group size and task complexity on deadline reactivity
International journal of industrial ergonomics, Vol.56, pp.106-114
11/2016
DOI: 10.1016/j.ergon.2016.09.011
Abstract
In contemporary organizations, people are often required to work and learn under increasing time pressures as organizations set deadlines in order to respond to stiffer competition. Human behavioral patterns in the presence of deadlines have been studied quantitatively to show that relatively little time is devoted to tasks early on and that most work is performed in close time proximity to a deadline. This phenomenon, called deadline rush, can be explained by a hyperbolic behavioral model. By employing a decision-making task based on an Anti-Air Warfare Coordinator (AAWC) simulator, the experiment had two group size levels (individuals and teams) and two task complexity levels (low and high). The experimental results showed that deadline reactivity is greater for individuals than teams on low-complexity tasks and task complexity is negatively related to deadline reactivity. The results of this study suggest that different group sizes and task types have a significant impact on production performance and that the setting of deadlines, to the degree possible, may be a relevant means towards managing or improving system performance.
•Low-complexity tasks induce higher reactivity to the deadlines than high-complexity tasks for both individuals and teams.•Individuals are more reactive than teams to low-complexity tasks.•Individuals, compared to teams, made more errors, meaning that high deadline reactivity is related to the production of more errors.
Details
- Title: Subtitle
- The effects of group size and task complexity on deadline reactivity
- Creators
- Ji-Eun Kim - Pennsylvania State UniversityDavid A Nembhard - Oregon State UniversityJung Hyup Kim - University of Missouri
- Resource Type
- Journal article
- Publication Details
- International journal of industrial ergonomics, Vol.56, pp.106-114
- Publisher
- Elsevier B.V
- DOI
- 10.1016/j.ergon.2016.09.011
- ISSN
- 0169-8141
- eISSN
- 1872-8219
- Language
- English
- Date published
- 11/2016
- Academic Unit
- Business Analytics; Industrial and Systems Engineering
- Record Identifier
- 9984187073402771
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