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Testing the Interaction Effects of Task Complexity in Computer Training Using the Social Cognitive Model
Author(s) -
Bolt Melesa Altizer,
Killough Larry N.,
Koh Hian Chye
Publication year - 2001
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.2001.tb00951.x
Subject(s) - task (project management) , computer science , structural equation modeling , construct (python library) , cognition , social cognitive theory , cognitive complexity , training (meteorology) , task analysis , outcome (game theory) , artificial intelligence , machine learning , psychology , social psychology , physics , management , neuroscience , meteorology , economics , programming language , mathematics , mathematical economics
Using a Modified Social Cognitive Theory framework, this study examines the behavior modeling and lecture‐based training approaches to computer training. It extends the existing Social Cognitive Model for computer training by adding the task complexity construct to training method, prior performance, computer self‐efficacy, outcome expectations, and performance. A sample of 249 students from a large state university served as participants in a laboratory experiment that was conducted to determine the task complexity*training method and task complexity* self‐efficacy interaction effects on performance. Structural equation modeling with interaction effects was used to analyze the data. The results show that behavior modeling outperforms lecture‐based training in a measure of final performance when task complexity is high. Further, it is found that computer self‐efficacy has a greater positive effect on performance when task complexity is high than when task complexity is low. Prior performance is also found to be an important variable in the model.