Abstract
ABSTRACTIn this study, we leverage valence theory, cognitive absorption theory, and IT adoption literature to investigate the perceptions of consumers towards the use of online Recommendation Agents (RAs) that vary in the number of details they provide in eliciting consumers’ preferences and presenting recommendations accordingly. The research model is empirically validated via an experiment involving 197 online shoppers. Results show that high in-depth RAs are better alternatives to low in-depth RAs in driving consumers’ intention to use RAs in their shopping experience. The findings provide novel insights for researchers and practitioners interested in understanding the proper design for online RAs.
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