An empirical study on the effectiveness of decision trees and decision tables for source-based decision validation

Effective decision-making requires models that are both understandable to users and capable of representing complex logic. Decision trees and decision tables are widely used graphical notations, yet prior research reports inconsistent findings on their relative effectiveness. This study addresses that gap through a controlled experiment with 69 business experts from a national education agency, comparing the comprehensibility and user acceptance of both notations. Drawing on Cognitive Fit Theory and the Technology Acceptance Model, it examines task accuracy alongside perceived ease of use and usefulness in source-based validation tasks. Decision tables are perceived as significantly more useful than decision trees, whereas perceived ease of use does not differ between the notations. Task accuracy is moderately positively correlated with perceived ease of use. Overall, both notations support decision validation, but decision tables’ higher perceived usefulness provides a meaningful basis for favoring them when user acceptance is a priority in source-based validation contexts.