Cognitive Engagement with a Multimedia ERP Training Tool: Assessing Computer Self-Efficacy and Technology Acceptance

Judy E. Scott; Steven Walczak

doi:10.1016/j.im.2008.10.003

Cognitive Engagement with a Multimedia ERP Training Tool: Assessing Computer Self-Efficacy and Technology Acceptance

Judy E. Scott, Steven Walczak

University of South Florida

Research output: Contribution to journal › Article › peer-review

Abstract

Computer self-efficacy (CSE) is a person's judgment of his or her ability to use a computer system. We investigated cognitive engagement, prior experience, computer anxiety, and organizational support as determinants of CSE in the use of a multimedia ERP system's training tool. We also examined the impact of CSE on its acceptance. We determined the benefits of a sequential multi-method approach using structural equation modeling and neural network analysis. High reliability predictions of individual CSE were achieved with a sequential multi-method approach. Specifically, we obtained almost 68% perfect CSE group prediction overall, with almost 85% perfect CSE group prediction using fuzzy sets and over 94% accuracy within one group classification. The resulting CSE assessment and classification enables management interventions, such as allocating users to appropriate instruction for more effective training.

Original language	American English
Journal	Information Management
Volume	46
DOIs	https://doi.org/10.1016/j.im.2008.10.003
State	Published - May 1 2009

Keywords

Computer self-efficacy
Cognitive engagement
Technology acceptance
ERP
Training tools
Neural network

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Cite this

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title = "Cognitive Engagement with a Multimedia ERP Training Tool: Assessing Computer Self-Efficacy and Technology Acceptance",

abstract = " Computer self-efficacy (CSE) is a person's judgment of his or her ability to use a computer system. We investigated cognitive engagement, prior experience, computer anxiety, and organizational support as determinants of CSE in the use of a multimedia ERP system's training tool. We also examined the impact of CSE on its acceptance. We determined the benefits of a sequential multi-method approach using structural equation modeling and neural network analysis. High reliability predictions of individual CSE were achieved with a sequential multi-method approach. Specifically, we obtained almost 68\% perfect CSE group prediction overall, with almost 85\% perfect CSE group prediction using fuzzy sets and over 94\% accuracy within one group classification. The resulting CSE assessment and classification enables management interventions, such as allocating users to appropriate instruction for more effective training.",

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N2 - Computer self-efficacy (CSE) is a person's judgment of his or her ability to use a computer system. We investigated cognitive engagement, prior experience, computer anxiety, and organizational support as determinants of CSE in the use of a multimedia ERP system's training tool. We also examined the impact of CSE on its acceptance. We determined the benefits of a sequential multi-method approach using structural equation modeling and neural network analysis. High reliability predictions of individual CSE were achieved with a sequential multi-method approach. Specifically, we obtained almost 68% perfect CSE group prediction overall, with almost 85% perfect CSE group prediction using fuzzy sets and over 94% accuracy within one group classification. The resulting CSE assessment and classification enables management interventions, such as allocating users to appropriate instruction for more effective training.

AB - Computer self-efficacy (CSE) is a person's judgment of his or her ability to use a computer system. We investigated cognitive engagement, prior experience, computer anxiety, and organizational support as determinants of CSE in the use of a multimedia ERP system's training tool. We also examined the impact of CSE on its acceptance. We determined the benefits of a sequential multi-method approach using structural equation modeling and neural network analysis. High reliability predictions of individual CSE were achieved with a sequential multi-method approach. Specifically, we obtained almost 68% perfect CSE group prediction overall, with almost 85% perfect CSE group prediction using fuzzy sets and over 94% accuracy within one group classification. The resulting CSE assessment and classification enables management interventions, such as allocating users to appropriate instruction for more effective training.

KW - Computer self-efficacy

KW - Cognitive engagement

KW - Technology acceptance

KW - ERP

KW - Training tools

KW - Neural network

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UR - https://doi.org/10.1016/j.im.2008.10.003

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M3 - Article

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JO - Information Management

JF - Information Management

ER -

Cognitive Engagement with a Multimedia ERP Training Tool: Assessing Computer Self-Efficacy and Technology Acceptance

Abstract

Keywords

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