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Extending the Technology Acceptance Model to assess automation

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Abstract

Often joint human–automation performance depends on the factors influencing the operator’s tendency to rely on and comply with automation. Although cognitive engineering (CE) researchers have studied automation acceptance as related to task–technology compatibility and human–technology coagency, information system (IS) researchers have evaluated user acceptance of technology, using the Technology Acceptance Model (TAM). The parallels between the two views suggest that the user acceptance perspective from the IS community can complement the human–automation interaction perspective from the CE community. TAM defines constructs that govern acceptance and provides a framework for evaluating a broad range of factors influencing technology acceptance and reliance. TAM is extensively used by IS researchers in various applications and it can be applied to assess the effect of trust and other factors on automation acceptance. Likewise, extensions to the TAM framework use the constructs of task–technology compatibility and past experience to extend its description of the role of human–automation interaction in automation adoption. We propose the Automation Acceptance Model (AAM) to draw upon the IS and CE perspectives and take into account the dynamic and multi-level nature of automation use, highlighting the influence of use on attitudes that complements the more common view that attitudes influence use.

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Acknowledgments

The authors would like to thank the members of the Cognitive Systems Laboratory (CSL) at the University of Wisconsin–Madison, for their helpful comments on earlier versions of this manuscript.

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Authors and Affiliations

  1. Department of Industrial and Systems Engineering, University of Wisconsin–Madison, Madison, WI, USA

    Mahtab Ghazizadeh & John D. Lee

  2. Departments of Industrial and Systems Engineering, Civil and Environmental Engineering, University of Washington, Seattle, WA, USA

    Linda Ng Boyle

  3. 3007 Mechanical Engineering, 1513 University Avenue, Madison, WI, 53706-1572, USA

    John D. Lee

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  1. Mahtab Ghazizadeh
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Correspondence to John D. Lee.

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Ghazizadeh, M., Lee, J.D. & Boyle, L.N. Extending the Technology Acceptance Model to assess automation. Cogn Tech Work 14, 39–49 (2012). https://doi.org/10.1007/s10111-011-0194-3

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