The impact of product visualization on easy comparison capability in online configurators

This thesis explores how different ways of showing products affect Easy Comparison Capability (EC) in online mass customization configurators. As more consumers compare several product options, being able to easily compare them has become important. While many configurators use advanced visualization technologies, their direct impact on EC is not well understood. This study fills that gap by analyzing how various visualization methods influence EC. The research looks at seven types of product visualization: 2D images, 3D models, 360-degree views, virtual images, video, product-in-motion, and photo-realistic images. EC is measured with a validated Likert scale, using data from 516 responses to a cross-sectional survey. Using a quantitative, deductive approach, the study applies descriptive statistics. The study uses a quantitative approach with descriptive statistics, boxplots, ANOVA, and regression analysis. Results show a clear pattern: dynamic formats like product-in-motion and 360-degree views have the highest EC scores, while 3D visualization scores lowest. Regression analysis confirms that visualization type strongly predicts EC, but gender has little effect. the model specification rather than greater algorithmic complexity. Interaction terms between modalities and contextual variables, such as product group, site, and configurator richness, improve model fit, while non-linear tree-based models show overfitting and weak out-of-sample performance. This thesis identifies visualization modality as an important factor for EC. It also highlights that dynamic and well-integrated visualization formats can help users compare products more effectively in online configurators.