Testing the performance of online recommendation agents: A meta-analysis

Abstract

Many retailers (e.g., Amazon, Walmart) use various types of online recommendation agents (RAs) on their websites to suggest goods and services to consumers. These RAs screen millions of options to ease consumers’ information search and evaluation. To determine which RA types best support consumers’ efforts, the present research reports a meta-analysis of perceived recommendation quality research, a key performance metric that gauges RAs from consumers’ perspectives. To test the framework derived from this meta-analysis, the authors rely on data gathered from 32,172 consumers, reported in 122 samples. The results affirm that some RAs perform better than others in leveraging the effects of perceived recommendation quality on consumers’ decision-making satisfaction, RA satisfaction, and intention to use the RA in the future. The best performing RAs feature specific algorithms (i.e., collaborative filtering, interactive RAs, and self-serving recommendations), recommendation presentations (i.e., solicited recommendation), and data sources (i.e., location-based and social network–based RAs). Moreover, the results suggest that some RAs perform better than others in leveraging the effects of decision-making and RA satisfaction on future use intentions. These insights advance RA theory and provide guidance for managers, with regard to choosing the optimal RA.