Controlling Visual Stimuli in Digital Tourism Experiments: A Replicable Framework

Abstract Digital tourism research increasingly uses visual stimuli to investigate attention, memory, evaluation, and decision-making, often within technology-mediated environments in which visual content forms part of the experimental setting. Yet stimulus-selection and control procedures are often insufficiently documented, and uncontrolled affective, perceptual, semantic, or technical variation may provide alternative explanations for observed effects. This paper presents a replicable framework for multidimensional control of naturalistic visual stimuli in digital tourism experiments while balancing ecological realism and experimental control. The framework was developed from the stimulus-control requirements of a digital tourism experimental design and was applied to 1,356 Nencki Affective Picture System (NAPS) images using normative metadata filtering, AI-supported semantic classification, rule-based decision criteria, technical normalisation, perceptual screening, expert validation, experimental allocation, and set-level comparison. The workflow produced 54 tourism images evenly distributed across Gastronomy, Nature, and Events and allocated to three category-balanced, non-overlapping experimental sets. These sets showed no statistically significant differences in the affective and low-level visual characteristics examined. The contribution lies in a transparent and auditable decision process integrating affective, perceptual, semantic, and technical stimulus control, providing a transferable methodological framework for strengthening the validity, comparability, and replicability of technology-mediated tourism experiments.