A Theoretical Model of Information Systems Analogical Learning

As organizations routinely replace information systems to support evolving business needs, employees must often adapt to new systems to complete familiar tasks. Despite widespread acknowledgment of the learning burden imposed by such transitions, the cognitive processes that guide users' adaptation, particularly how prior system knowledge is leveraged, remain under-theorized in the information systems literature. This paper introduces Systems Analogy Learning Theory (SALT), a cognitive framework grounded in analogical learning theory, to explain how users draw on mental models of a prior (base) system to make sense of a new (target) system. SALT outlines a process of analogical reasoning composed of access, mapping, and transfer, and identifies four prototypical learning pathways based on users’ perceived surface and structural similarities between systems. These pathways are theorized to yield distinct learning trajectories and implications for training and system design.

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Publication Details

Journal
ScholarSpace (University of Hawaii at Manoa)
Published
2025-12-23
Primary Topic
Complex Systems and Decision Making
Type
article
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article

A Theoretical Model of Information Systems Analogical Learning

Kelly J. Fadel, Robert Mills, Reagan R. Siggard
ScholarSpace (University of Hawaii at Manoa)
Complex Systems and Decision Making
article

A Theoretical Model of Information Systems Analogical Learning

Kelly J. Fadel, Robert Mills, Reagan R. Siggard
article en

Abstract

As organizations routinely replace information systems to support evolving business needs, employees must often adapt to new systems to complete familiar tasks. Despite widespread acknowledgment of the learning burden imposed by such transitions, the cognitive processes that guide users' adaptation, particularly how prior system knowledge is leveraged, remain under-theorized in the information systems literature. This paper introduces Systems Analogy Learning Theory (SALT), a cognitive framework grounded in analogical learning theory, to explain how users draw on mental models of a prior (base) system to make sense of a new (target) system. SALT outlines a process of analogical reasoning composed of access, mapping, and transfer, and identifies four prototypical learning pathways based on users’ perceived surface and structural similarities between systems. These pathways are theorized to yield distinct learning trajectories and implications for training and system design.

ScholarSpace (University of Hawaii at Manoa)
Openalex Percentile: Top 28%
Complex Systems and Decision Making
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