Parametric search engines: What makes them effective when shopping online for differentiated products?

Many online retailers try to assist consumers via parametric search engines (PSEs), i.e., attribute-based search engines. The objective of this study is to model the effectiveness of four PSEs in using search effort and domain knowledge to increase decision quality, decision confidence, perceived ease of use and perceived usefulness. Our model comprises a set of behavioral decision theory antecedents to the technology acceptance model. We tested users with four PSEs in a laboratory experiment and modeled the results with partial least squares (PLS) analysis. The main result of this paper is a PLS model showing that the effects of search effort and domain knowledge are mediated through decision quality and decision confidence to impact perceived ease of use and perceived usefulness. The model explains the variance in decision quality (17.3%), decision confidence (28.3%) and perceived usefulness (27.0%). Overall, this study shows that input, process and outcome variables are important for predicting the effectiveness of PSEs. Implications for research and practice are discussed.

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