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Correlations Between Computer-Related Causal Attributions and User Persistence

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Advances in Artificial Intelligence, Software and Systems Engineering (AHFE 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 787))

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Abstract

This study used data collected from 2270 participants to investigate the impact of computer-related causal attributions on users’ persistence. Attribution theory deals with subjectively perceived causes of events and is commonly used for explaining and predicting human behavior, emotion, and motivation. Individual attributions may either positively or negatively influence one’s learning behavior, confidence levels, effort, or motivation. Results indicate that attributions indeed influence users’ persistence in computer situations. Users with favorable attribution styles exhibit greater levels of persistence than users with unfavorable attribution styles. The findings can be used in HCI research and practice to understand better why users think, feel, or behave in a certain way. It is argued that an understanding of users’ attributional characteristics is valuable for developing and improving existing computer learning training strategies and methods, as well as support and assistance mechanisms.

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Authors and Affiliations

  1. Faculty of Electrical Engineering and Computer Science, Lübeck University of Applied Sciences, Mönkhofer Weg 239, 23562, Lübeck, Germany

    Adelka Niels, Sophie Jent & Monique Janneck

  2. ZukunftsDesign, Coburg University of Applied Sciences, Friedrich-Streib-Strasse 2, 96450, Coburg, Germany

    Christian Zagel

Authors
  1. Adelka Niels
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  2. Sophie Jent
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  3. Monique Janneck
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  4. Christian Zagel
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Corresponding author

Correspondence to Christian Zagel .

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Editors and Affiliations

  1. University of Central Florida, Orlando, Florida, USA

    Tareq Z. Ahram

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Niels, A., Jent, S., Janneck, M., Zagel, C. (2019). Correlations Between Computer-Related Causal Attributions and User Persistence. In: Ahram, T. (eds) Advances in Artificial Intelligence, Software and Systems Engineering. AHFE 2018. Advances in Intelligent Systems and Computing, vol 787. Springer, Cham. https://doi.org/10.1007/978-3-319-94229-2_23

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