The Effects of Interruptions and Information Overload on Decision-Making Performance in Knowledge-Work
Year: 2015
Type: article
Abstract: Research suggests that interruptions during a cognitive task can affect the quality and timeliness of decision-making in knowledge-intensive work environments.Moreover, information overload can lead to lower-quality and slower decision-making.This research introduces and tests "emphasis framing" as an operational tactic to help mitigate the effects of information overload and interruptions on the quality and timeliness of decision-making in knowledge-intensive work environments.A series of three experiments was conducted with the following participants: students, crowd sourcing participants, and emergency department physicians.In our studies with students and crowdsourcing participants, while our results were interesting, we were unable to attain statistically significant results.But the results of these experiments did illustrate that studying the complex cognitive tasks associated with knowledge work is nontrivial and highlighted the unique challenges introduced by the knowledge workers themselves and needs to be further explored.Additionally, the experiment with the crowdsourcing participants illustrated some of the challenges with conducting behavioral, knowledge-intensive experiments on crowdsourcing sites and highlighted that further research is needed to determine if that platform is appropriate for these types of experiments.We did attain statistically significant results on our experiment with emergency department physicians.We measured the effect of emphasis framing on two operational performance metrics when under information overload: (1) the quality (accuracy) of the physician's clinical evaluation, and (2) the efficiency (timeliness) of his/her clinical decision-making.Our results showed that emphasis framing helped mitigate the effects of information overload and increased the quality of
Author: Lauren F. Laker
Institution: University of Cincinnati
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2515645758