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Paper Type
ERF
Abstract
Mobile devices provide opportunities for real-time data analysis and quick decision-making to occur. However, challenges can arise because of certain limitations such as small screen sizes. In addition, the time pressure experienced when making urgent decisions in a time-constrained task can generate stress, which may lead to less than optimal decision outcomes. Through the lens of cognitive load theory, we propose that mobile devices can increase problem-solving accuracy and reduce problem-solving efforts in a time sensitive context by forcing the user to identify and cognitively process only relevant information. Using a laboratory experiment, we plan to assess problem-solving accuracy and perceived efforts to complete data analytics tasks with mobile devices versus desktop computers in time-constrained contexts. The findings of this study can provide insights into potential improvements in performance by using mobile devices for time-limited data analytics tasks, as well as provide guidance for future research in this domain.
Recommended Citation
Eschenbrenner, Brenda L. and McGill, Rachel, "Using Mobile Devices for Data Analytics: The Impact of Time Pressure" (2020). AMCIS 2020 Proceedings. 16.
https://aisel.aisnet.org/amcis2020/data_science_analytics_for_decision_support/data_science_analytics_for_decision_support/16
Using Mobile Devices for Data Analytics: The Impact of Time Pressure
Mobile devices provide opportunities for real-time data analysis and quick decision-making to occur. However, challenges can arise because of certain limitations such as small screen sizes. In addition, the time pressure experienced when making urgent decisions in a time-constrained task can generate stress, which may lead to less than optimal decision outcomes. Through the lens of cognitive load theory, we propose that mobile devices can increase problem-solving accuracy and reduce problem-solving efforts in a time sensitive context by forcing the user to identify and cognitively process only relevant information. Using a laboratory experiment, we plan to assess problem-solving accuracy and perceived efforts to complete data analytics tasks with mobile devices versus desktop computers in time-constrained contexts. The findings of this study can provide insights into potential improvements in performance by using mobile devices for time-limited data analytics tasks, as well as provide guidance for future research in this domain.
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