Examining the impacts of mental workload and task-technology fit on user acceptance of the social media search system

Yan (Mandy) Dang (), Yulei (Gavin) Zhang (), Susan A. Brown () and Hsinchun Chen ()
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Yan (Mandy) Dang: Northern Arizona University
Yulei (Gavin) Zhang: Northern Arizona University
Susan A. Brown: University of Arizona
Hsinchun Chen: University of Arizona

Information Systems Frontiers, 2020, vol. 22, issue 3, No 11, 697-718

Abstract: Abstract Information overload has been an important issue in today’s big data era where a huge amount of unstructured user-generated content in different languages is being created on the Web in every minute. Social media search systems could help with it by effectively and efficiently collecting, storing, organizing and presenting user-generated content across the Web in an organized and timely manner. However, little research has been done to examine factors that could influence user acceptance on this new type of systems. To address it, this study develops a research model by integrating Mental Workload (MWL), Task-Technology Fit (TTF), and the unified theory of acceptance and use of technology (UTAUT). The model is tested on a security-related social media search system. The results indicate that both MWL and TTF can significantly influence user acceptance. We also operationalize the multi-dimensional latent construct of MWL by developing survey-based measurement items for different dimensions.

Keywords: Mental workload (MWL); Task-technology fit (TTF); User-generated content; User acceptance; Social media search system (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10796-018-9879-y

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