Reconsidering the Role of Design Standards in Developing Effective Safety Labeling: Monolithic Recipes or Collections of Separable Features?

Abstract

Objective

This research investigated whether safety labeling design guidelines, such as the American National Standards Institute (ANSI) Z535 series, contribute to better warnings.

Background

Studies investigating the impact of safety label formatting on warning effectiveness have produced mixed findings. Additionally, research has failed to find a consistent relationship between measures of predicted and actual compliance. One commonality is that all of these studies have investigated the ANSI Z535 guidelines as a binary variable rather than as an integrative system of separable features.

Method

We measured predicted compliance using both a within-subjects and a between-subjects design, but actual compliance using only a between-subjects design. Data were analyzed using both analysis of variance and linear/probit regressions to test the relationships between warning features recommended in the ANSI Z535 guidelines and measures of behavioral compliance.

Results

Predicted compliance assessed via a within-subjects design differed greatly from predicted compliance assessed via a between-subjects design. Levels of predicted and actual compliance were most similar when both measures were assessed using a between-subjects design. Consistent with previous research, location had a strong relationship with actual compliance, but surprisingly, presence of an ANSI-style orange warning header had a negative relationship with compliance.

Conclusion

The choice of experimental design and analytical methods can dramatically influence a study’s results and conclusions drawn. This research identified several aspects of experimental design that should be considered in future research on warning effectiveness.

Application

Testing features recommended in the ANSI Z535 guidelines under varying conditions can contribute to the development of more effective warnings.

Keywords

warning systems warnings warning compliance experimental design statistics data analysis

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