The Effect of Sensitivity Analysis on the Usage of Recommender Systems

Recommender systems have become a valuable tool for successful e-commerce. The quality of their recommendations depends heavily on how precisely consumers are able to state their preferences. However, empirical evidence has shown that the preference construction process is highly aected by uncertainties. This has a negative impact on the robustness of recommendations. If users perceive a lack of accuracy in the recommendation of recommender systems, this reduces their condence in the recommendation generating process. This in turn negatively inuences the adoption of recommender systems. We argue in this paper that sensitivity analysis is able to overcome this problem. Although sensitivity analysis has already been well studied, it was ignored to a large extent in the eld of recommender systems. To close this gap, we propose a research model that shows how a sensitivity analysis and the presence of uncertainties inuence decision condence and the intention to use recommender systems.