Towards explaining user satisfaction with contact tracing mobile applications in a time of pandemic: a text analytics approach

Date
2022-01-04
Authors
Namvar, Morteza
Akhlaghpour , Saeed
Pool, Javad
Priscilia , Anisa
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This research project investigates the critical phenomenon of the post-adoption use of Contact Tracing Mobile Applications (CTMAs) in a time of pandemic. A panel data set of customer reviews was collected from March 2020 to June 2021. Using sentiment analysis, topic modeling and dictionary-based analytics, 10,337 reviews were analyzed. The results show that after controlling for review sentiment and length, user satisfaction is associated with users’ perception of utilitarian benefits of CTMA, their CTMA-specific privacy concerns, and installation and use issues. Our methodological approach (using various text analysis techniques for analyzing public feedback) and findings (influential factors on consumers’ satisfaction with CTMA) can inform the design and deployment of the next generation of CTMAs for managing future pandemics.
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Digital Government and AI, contact tracing, covid-19, online reviews, text analytics, user satisfaction
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10 pages
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Proceedings of the 55th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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