Recommendation Agents aim at reducing decision effort and improving quality through recommending a set of alternatives which fits users’ preferences. The list of the alternatives is typically compiled in a multidimensional way, where the user is called to indicate preferences regarding a number of different product dimensions. Nevertheless, in decision situations where products are characterized by a variety of advantages and disadvantages, consumers may be confronted with conflicting values of product attributes, leading to severe dilemmas and decision paralysis. Based on theories on choice context effects, it is proposed that the relationship between the attributes of the recommended products impact perceptions regarding the quality of the decision process as well as the quality of decision outcomes, including the acceptance of the technology. This study proposes a novel method of determining the presentation of the recommended alternatives and suggests a system design which minimizes decision difficulty and maximizes both user’s satisfaction and the agent’s use.
Fytraki, Agapi Thaleia; Dellaert, Benedict G.C.; and Benbasat, Izak, "Recommendation Agent Acceptance: The Impact of Decision Difficulty in RA Sets of Multidimensional Products" (2014). DIGIT 2014 Proceedings. 3.