The purpose of this paper is to report the results of a laboratory experiment that investigated how assortment planners’ perceptions, usage behavior, and decision quality are influenced by the way recommendations of an artificial intelligence (AI)-based recommendation agent (RA) are presented. A within-subject laboratory experiment was conducted with twenty subjects. Participants perceptions and usage behavior toward an RA while making decisions were assessed using validated measurement scales and eye-tracking technology. The results of this study show the importance of a transparent RA demanding less cognitive effort to understand and access the explanations of a transparent RA on assortment planners’ perceptions (i.e., source credibility, sense of control, decision quality, and satisfaction), usage behavior, and decision quality. Results from this study suggest that designing RAs with more transparency for the users bring perceptual and attitudinal benefits that influence both the adoption and continuous use of those systems by employees. This study contributes to filling the literature gap on RAs in organizational contexts, thus advancing knowledge in the human–computer interaction literature. The findings of this study provide guidelines for RA developers and user experience (UX) designers on how to best create and present an AI-based RA to employees.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.