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EMAC 2019 Annual Conference


imagefluency: Image Fluency Scores in R
(A2019-9410)

Published: May 28, 2019

AUTHORS

Stefan Mayer, University of Tübingen

KEYWORDS

visual stimuli; image features; processing fluency

ABSTRACT

Given the vast amount of unstructured data that is nowadays available, more and more marketing researchers emphasize the need to use and interpret unstructured data in marketing models. In recent years, especially images are recognized as a potential source of valuable information, as vision is the key sense that guides us through our life. At the same time, research in psychology has shown that even simple image features like contrast or complexity can have a tremendous effect on a variety of human judgments through processing fluency. This research describes a newly created R package that converts unstructured information contained in images into structured image features. Importantly, these image features are not constructed in a black-box-model but are naturally interpretable. They might prove helpful not only for stimulus selection in experimental research, but also as a way to include image information in marketing models, thereby increasing a model's statistical and predictive power.